@article{Sowmya,
abstract = {-Usually underwater images will have a less visibility conditions, for which the 3D reconstruction for these images has become a challenging tasks. This is because of problems like harsh environmental conditions (black smoke, white smoke) or may be of infraction by floating partials and aquatic animals. The other problem that arises like light propagation in underwater which affects the image color, and the problem in visualization of small objects is poor.to overcome these problems in this paper, we did the detailed survey on different technique of 3D reconstruction of underwater objects.},
annote = {NULL},
author = {Sowmya, S G and Dixit, Sunanda},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Sowmya, Dixit - Unknown - 3D Reconstruction Methodologies a Review.pdf:pdf},
issn = {0975-0282},
journal = {International Journal of Advanced Networking {\&} Applications (IJANA)},
keywords = {-SFM,CLAHE,Delaunay Triangulation,SIFT,Speeded Up Robust Feature},
mendeley-groups = {3D Reconstruction},
pages = {2--4},
title = {{3D Reconstruction Methodologies: a Review}}
}
@misc{FARO,
author = {FARO},
mendeley-groups = {3D Reconstruction},
title = {{FARO Focus}},
url = {http://www.faro.com/en-us/products/3d-surveying/faro-focus3d/features{\#}main},
urldate = {2017-05-30}
}
@misc{Intel,
author = {Intel},
mendeley-groups = {3D Reconstruction},
title = {{Intel RealSense Camera}},
url = {https://software.intel.com/en-us/realsense/sr300camera},
urldate = {2017-05-30}
}
@article{Yan,
abstract = {Understanding the 3D world is a fundamental problem in computer vision. How-ever, learning a good representation of 3D objects is still an open problem due to the high dimensionality of the data and many factors of variation involved. In this work, we investigate the task of single-view 3D object reconstruction from a learning agent's perspective. We formulate the learning process as an interaction between 3D and 2D representations and propose an encoder-decoder network with a novel projection loss defined by the perspective transformation. More importantly, the projection loss enables the unsupervised learning using 2D observation without explicit 3D supervision. We demonstrate the ability of the model in generating 3D volume from a single 2D image with three sets of experiments: (1) learning from single-class objects; (2) learning from multi-class objects and (3) testing on novel object classes. Results show superior performance and better generalization ability for 3D object reconstruction when the projection loss is involved.},
annote = {NULL},
author = {Yan, Xinchen and Yang, Jimei and Yumer, Ersin and Guo, Yijie and Lee, Honglak},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Yan et al. - Unknown - Perspective Transformer Nets Learning Single-View 3D Object Reconstruction without 3D Supervision.pdf:pdf},
mendeley-groups = {3D Reconstruction},
title = {{Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision}},
url = {https://arxiv.org/pdf/1612.00814.pdf?origin=publication{\_} detail{\&}ev=pub{\_}int{\_}prw{\_}xdl{\&}msrp=KAcRLIOw4ylGj0Dg5 af8qCC1Xiao1n-q29brSOcbnfBkpa58p003MaCGUU30PQ9 0L1Lv1HMQlnFZb-ea cq{\_}Jq7W3fonb1RZyXh2SPibmHt8. c6FiySAgohYp1fNzyGbwxVJmiBtnS 6YXB9r1mQU9aVX-xDfP4PCoLUSe6lAM}
}
@article{M2017,
author = {M, Gutierrez-villalobos Jose and C, Mora-vazquez Julio},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/M, C - 2017 - Simple and low cost scanner 3D system based on a Time-of-Flight ranging sensor.pdf:pdf},
isbn = {9781538616949},
keywords = {3d-reconstruction,a diffracted,can,figure digitizer,infrared scanning system,other types of 3d,provide,reconstruction,scanner are based on,surface,systems are dependent on,the resolution that sensor,time-fly detector},
mendeley-groups = {3D Reconstruction},
pages = {3--7},
title = {{Simple and low cost scanner 3D system based on a Time-of-Flight ranging sensor}},
year = {2017}
}
@article{Zhang2017,
abstract = {IEEE We present a practical and inexpensive method to reconstruct 3D scenes that include transparent and mirror objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, which commonly include glass. These large structures are often invisible to cameras. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-sized reconstruction setting. Our simple hardware setup augments a regular depth camera with a single ultrasonic sensor, which is able to measure the distance to any object, including transparent surfaces. The key technical challenge is the sparse sampling rate from the acoustic sensor, which only takes one point measurement per frame. To address this challenge, we take advantage of the fact that the large scale glass structures in indoor environments are usually either piece-wise planar or simple parametric surfaces. Based on these assumptions, we have developed a novel sensor fusion algorithm that first segments the (hybrid) depth map into different categories such as opaque/transparent/infinity (e.g., too far to measure) and then updates the depth map based on the segmentation outcome. We validated our algorithms with a number of challenging cases, including multiple panes of glass, mirrors, and even a curved glass cabinet.},
author = {Zhang, Yu and Ye, Mao and Manocha, Dinesh and Yang, Ruigang},
doi = {10.1109/TPAMI.2017.2723883},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Zhang et al. - 2017 - 3D Reconstruction in the Presence of Glass and Mirrors by Acoustic and Visual Fusion.pdf:pdf},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {3D reconstruction,Acoustics,Cameras,Glass,Robot sensing systems,Surface reconstruction,Three-dimensional displays,Ultrasonic imaging,sensor fusion,transparent/mirrored surface modeling,ultrasonic range finding},
mendeley-groups = {3D Reconstruction},
number = {c},
title = {{3D Reconstruction in the Presence of Glass and Mirrors by Acoustic and Visual Fusion}},
volume = {8828},
year = {2017}
}
@article{Fan2017,
author = {Fan, Baijiang and Rao, Yunbo and Liu, Wei and Wang, Qifei},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Fan et al. - 2017 - Region-Based Growing Algorithm for 3D Reconstruction from MRI Images.pdf:pdf},
isbn = {9781509062386},
keywords = {-3d reconstruction,image,multiple angle observation,region-based growing,segmentation},
mendeley-groups = {3D Reconstruction},
pages = {521--525},
title = {{Region-Based Growing Algorithm for 3D Reconstruction from MRI Images}},
year = {2017}
}
@article{Wang2017,
annote = {NULL},
author = {Wang, Chao and Guo, Xiaohu},
doi = {10.1007/s41095-016-0072-2},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Wang, Guo - 2017 - Feature-based RGB-D camera pose optimization for real-time 3D reconstruction.pdf:pdf},
issn = {2096-0433},
journal = {Computational Visual Media},
keywords = {camera pose optimization,feature,matching,real-time 3d reconstruction},
mendeley-groups = {3D Reconstruction},
pages = {1--12},
title = {{Feature-based RGB-D camera pose optimization for real-time 3D reconstruction}},
url = {http://link.springer.com/10.1007/s41095-016-0072-2},
year = {2017}
}
@article{Harjoko2017,
author = {Harjoko, Agus and Hujja, Roghib Muhammad and Awaludin, Lukman},
doi = {10.1109/ICSIGSYS.2017.7967057},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Harjoko, Hujja, Awaludin - 2017 - Low-cost 3D surface reconstruction using Stereo camera for small object.pdf:pdf},
isbn = {978-1-5090-6748-0},
journal = {2017 International Conference on Signals and Systems (ICSigSys)},
keywords = {5,camera,can be observed in,data,field can be used,in the field of,light structured,objects,organ,s shape,stereo,surface reconstruction of 3d,texture,the form of digital,then,to reconstruct the human},
mendeley-groups = {3D Reconstruction},
pages = {285--289},
title = {{Low-cost 3D surface reconstruction using Stereo camera for small object}},
url = {http://ieeexplore.ieee.org/document/7967057/},
year = {2017}
}
@article{Burns2017,
author = {Burns, Calum},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Burns - 2017 - Texture Super-Resolution for 3D Reconstruction.pdf:pdf},
isbn = {9784901122160},
mendeley-groups = {3D Reconstruction},
pages = {4--7},
title = {{Texture Super-Resolution for 3D Reconstruction}},
year = {2017}
}
@article{Group2017,
author = {Group, Machine Learing and Intel, Movidius and Ireland, Dublin and Palla, Alessandro and Moloney, David and Fanucci, Luca},
file = {:Users/hanryham/Downloads/08248355.pdf:pdf},
isbn = {9781538611074},
mendeley-groups = {3D Reconstruction},
number = {Icsai},
pages = {566--575},
title = {{Fully Convolutional Denoising Autoencoder for 3D Scene Reconstruction from a single depth image}},
year = {2017}
}
@article{Bondi2016,
abstract = {Performing face recognition across 3D scans with different resolution is now attracting an increasing interest thanks to the introduction of a new generation of depth cameras, capable of acquiring color/depth images over time. In fact, these devices acquire and provide depth data with much lower resolution compared with the 3D high-resolution scanners typically used for face recognition applications. If data are acquired without user cooperation, the problem is even more challenging, and the gap of resolution between probe and gallery scans can yield to a severe loss in terms of recognition accuracy. Based on these premises, we propose a method to build a higher resolution 3D face model from 3D data acquired by a low-resolution scanner. This face model is built using data acquired when a person passes in front of the scanner, without assuming any particular cooperation. The 3D data are registered and filtered by combining a model of the expected distribution of the acquisition error with a variant of the lowess method to remove outliers and build the final face model. The proposed approach is evaluated in terms of accuracy of face reconstruction and face recognition.},
author = {Bondi, Enrico and Pala, Pietro and Berretti, Stefano and {Del Bimbo}, Alberto},
doi = {10.1109/TIFS.2016.2601059},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Bondi et al. - 2016 - Reconstructing high-resolution face models from Kinect Depth Sequences.pdf:pdf},
isbn = {978-1-4799-6026-2},
issn = {15566013},
journal = {IEEE Transactions on Information Forensics and Security},
keywords = {Kinect depth camera,face recognition,increased resolution,locally weighted regression,manifold estimation},
mendeley-groups = {3D Reconstruction},
number = {12},
pages = {2843--2853},
title = {{Reconstructing high-resolution face models from Kinect Depth Sequences}},
volume = {11},
year = {2016}
}
@article{Anwer2016,
annote = {NULL},
author = {Anwer, Atif and Ali, Syed Saad Azhar and Meriaudeau, Fabrice},
doi = {10.1109/ICIAS.2016.7824132},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Anwer, Ali, Meriaudeau - 2016 - Underwater online 3D mapping and scene reconstruction using low cost kinect RGB-D sensor.pdf:pdf},
isbn = {978-1-5090-0845-2},
journal = {2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)},
mendeley-groups = {3D Reconstruction},
pages = {1--6},
title = {{Underwater online 3D mapping and scene reconstruction using low cost kinect RGB-D sensor}},
url = {http://ieeexplore.ieee.org/document/7824132/},
year = {2016}
}
@article{Wu2016,
abstract = {Figure 1: Our anatomically-constrained local face model allows for high-quality and very expressive monocular performance capture from a large variety of input sources, including high-speed capture of extreme deformations caused by external forces such as wind, high-quality studio setups, or unconstrained outdoor acquisition using helmet-mounted GoPro and handheld iPhone devices. Abstract We present a new anatomically-constrained local face model and fitting approach for tracking 3D faces from 2D motion data in very high quality. In contrast to traditional global face models, often built from a large set of blendshapes, we propose a local deformation model composed of many small subspaces spatially distributed over the face. Our local model offers far more flexibility and expres-siveness than global blendshape models, even with a much smaller model size. This flexibility would typically come at the cost of reduced robustness, in particular during the under-constrained task of monocular reconstruction. However, a key contribution of this work is that we consider the face anatomy and introduce subspace skin thickness constraints into our model, which constrain the face to only valid expressions and helps counteract depth ambiguities in monocular tracking. Given our new model, we present a novel fitting optimization that allows 3D facial performance reconstruction from a single view at extremely high quality, far beyond previous fitting approaches. Our model is flexible, and can be applied also when only sparse motion data is available, for example with marker-based motion capture or even face posing from artistic sketches. Further-more, by incorporating anatomical constraints we can automatically estimate the rigid motion of the skull, obtaining a rigid stabilization of the performance for free. We demonstrate our model and single-view fitting method on a number of examples, including, for the first time, extreme local skin deformation caused by external forces such as wind, captured from a single high-speed camera.},
annote = {NULL},
author = {Wu, Chenglei and Bradley, Derek and Gross, Markus and Beeler, Thabo},
doi = {10.1145/2897824.2925882},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Wu et al. - 2016 - An Anatomically-Constrained Local Deformation Model for Monocular Face Capture.pdf:pdf},
isbn = {9781450342797},
issn = {15577368},
journal = {ACM Trans. Graph. Article},
keywords = {Monocular face tracking,anatomical constraints,facial performance capture.,local face model},
mendeley-groups = {3D Reconstruction},
number = {12},
pages = {1--12},
title = {{An Anatomically-Constrained Local Deformation Model for Monocular Face Capture}},
url = {http://dx.doi.org/10.1145/2897824.2925882},
volume = {35},
year = {2016}
}
@article{Zhang2016,
author = {Zhang, Kun and Qiao, Shiquan and Gao, Kai},
doi = {10.1109/ICMIC.2015.7409387},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Zhang, Qiao, Gao - 2016 - A new point cloud reconstruction algorithm based-on geometrical features.pdf:pdf},
isbn = {9780956715753},
journal = {Proceedings of 2015 7th International Conference on Modelling, Identification and Control, ICMIC 2015},
keywords = {geometrical features,point clouds,reverse engineering,set theory,surface reconstruction},
mendeley-groups = {3D Reconstruction},
number = {Icmic},
pages = {3--8},
title = {{A new point cloud reconstruction algorithm based-on geometrical features}},
year = {2016}
}
@article{Ravanelli2016,
abstract = {{\textless}p{\textgreater}Today range cameras are widespread low-cost sensors based on two different principles of operation: we can distinguish between Structured Light (SL) range cameras (Kinect v1, Structure Sensor, ...) and Time Of Flight (ToF) range cameras (Kinect v2, ...). Both the types are easy to use 3D scanners, able to reconstruct dense point clouds at high frame rate. However the depth maps obtained are often noisy and not enough accurate, therefore it is generally essential to improve their quality. Standard RGB cameras can be a valuable solution to solve such issue. The aim of this paper is therefore to evaluate the integration feasibility of these two different 3D modelling techniques, characterized by complementary features and based on standard low-cost sensors. {\textless}br{\textgreater}{\textless}br{\textgreater} For this purpose, a 3D model of a DUPLO{\textless}sup{\textgreater}TM{\textless}/sup{\textgreater} bricks construction was reconstructed both with the Kinect v2 range camera and by processing one stereo pair acquired with a Canon Eos 1200D DSLR camera. The scale of the photgrammetric model was retrieved from the coordinates measured by Kinect v2. The preliminary results are encouraging and show that the foreseen integration could lead to an higher metric accuracy and a major level of completeness with respect to that obtained by using only separated techniques.{\textless}/p{\textgreater}},
annote = {Active apporach employee an imaging sensor in order to sense the environment. Apart of that, the price of this camera is low as well. This type of camera can be used as 3D scanner, generating 3D cloud points. However the depth map produced are generally noisy. For instance, kinect is of this approach.

On the other hand, an established stereo vision technique suffers incomplete 3D models in low texture regions. In order to produce cloud points that act as 3D scanner required a further computation to produce depth map.

Ravanelli et al. introduced a combination of RGB-D and stereo images in order to enhance depth map. They have done a preliminary test, to check two different 3D modelling techniques using range imaging and traditional stereo vision. They employee kinect V2 and stereo camera using EOS 1200D DSLR. The two 3D models are combined using CloudCompare (Girardeau-Montaut, 2016) 3D Point cloud and mesh processing software. Subsequently, the ICP algorithm is performed to estimate the parameters of the residual roto-translation with scale transformation from the same reference system of two point clouds. In order to evaluate the proposed methods, DUPLO bricks construction is used. The results show that integrated approach achieve a higher metric accuracy of the final 3D model.},
author = {Ravanelli, R. and Nascetti, A. and Crespi, M.},
doi = {10.5194/isprsarchives-XLI-B5-699-2016},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Ravanelli, Nascetti, Crespi - 2016 - Kinect V2 and Rgb Stereo Cameras Integration for Depth Map Enhancement.pdf:pdf},
issn = {2194-9034},
journal = {ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
keywords = {3d modelling,range cameras,real-time,rgb stereo cameras,sensor integration},
mendeley-groups = {3D Reconstruction},
number = {July},
pages = {699--702},
title = {{Kinect V2 and Rgb Stereo Cameras Integration for Depth Map Enhancement}},
url = {http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B5/699/2016/isprs-archives-XLI-B5-699-2016.pdf},
volume = {XLI-B5},
year = {2016}
}
@article{Assoc2016,
author = {Assoc, Supervisor and Gholamreza, Prof},
file = {:Users/hanryham/Downloads/atprog-courses-bakalaureuset55-loti.05.029-lembit-valgma-text-20160520.pdf:pdf},
mendeley-groups = {3D Reconstruction},
title = {{Lembit Valgma 3D reconstruction using Kinect v2 camera}},
year = {2016}
}
@article{Xu2016,
abstract = {High-fidelity reconstruction of complete objects is known as a particularly challenging problem. In this paper, we provide a system for object modeling with Kinect and rotation platform. Our modeling solution is divided into three steps: (1) system calibration, (2) Point cloud real time registration,(3) Mesh surface reconstruction and automatic texture mapping. We propose a easy-to-use calibration method for the first step, and based on the calibration result, a automatic texture-independent 3D point clouds registration method is presented. Finally, triangulate meshing and automatic texture mapping technique are used to recreate complete mesh model from the integrated point clouds. Our system is validated by the modeling of several real-world objects, and all the results show its effectiveness.},
author = {Xu, Huarong and Wang, Xiaoming and Shi, Limin},
doi = {10.1109/RVSP.2015.19},
file = {:Users/hanryham/Downloads/07399143.pdf:pdf},
isbn = {9781467396462},
journal = {Proceedings - 2015 3rd International Conference on Robot, Vision and Signal Processing, RVSP 2015},
keywords = {3D object modeling,Kinect,rotation platform,weak texture},
mendeley-groups = {3D Reconstruction},
pages = {43--46},
title = {{Fast 3D-Object Modeling with Kinect and Rotation Platform}},
year = {2016}
}
@article{Papadopoulos2016,
author = {Papadopoulos, Georgios and Daras, Petros},
doi = {10.1109/TCSVT.2016.2643161},
file = {:Users/hanryham/Downloads/07792623.pdf:pdf},
issn = {1051-8215},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
mendeley-groups = {3D Reconstruction},
number = {c},
pages = {1--1},
title = {{Human Action Recognition Using 3D Reconstruction Data}},
url = {http://ieeexplore.ieee.org/document/7792623/},
volume = {8215},
year = {2016}
}
@article{Liu2016,
abstract = {Depth maps captured by Kinect depth cameras are being widely used for 3D action recognition. However, such images often appear noisy and contain missing pixels or black holes. This paper presents a computationally efficient method for both denoising and hole-filling in depth images. The denoising is achieved by utilizing a combination of Gaussian kernel filtering and anisotropic filtering. The hole-filling is achieved by utilizing a combination of morphological filtering and zero block filtering. Experimental results using the publicly available datasets are provided indicating the superiority of the developed method in terms of both depth error and computational efficiency compared to three existing methods.},
author = {Liu, Soulan and Chen, Chen and Kehtarnavaz, Nasser},
doi = {10.1117/12.2230495},
file = {:Users/hanryham/Downloads/SPIE2016.pdf:pdf},
isbn = {9781510601420},
issn = {1996756X},
keywords = {computationally efficient depth image,depth image denoising,depth image hole filling,enhancement},
mendeley-groups = {3D Reconstruction},
pages = {98970V},
title = {{A computationally efficient denoising and hole-filling method for depth image enhancement}},
url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi =10.1117/12.2230495},
volume = {9897},
year = {2016}
}
@article{Firman2016,
abstract = {Building a complete 3D model of a scene, given only a single depth image, is underconstrained. To gain a full vol- umetric model, one needs either multiple views, or a single view together with a library of unambiguous 3D models that will fit the shape of each individual object in the scene. We hypothesize that objects of dissimilar semantic classes often share similar 3D shape components, enabling a limited dataset to model the shape of a wide range of ob- jects, and hence estimate their hidden geometry. Exploring this hypothesis, we propose an algorithm that can complete the unobserved geometry of tabletop-sized objects, based on a supervised model trained on already available volumetric elements. Our model maps from a local observation in a single depth image to an estimate of the surface shape in the surrounding neighborhood. We validate our approach both qualitatively and quantitatively on a range of indoor object collections and challenging real scenes.},
author = {Firman, Michael and Aodha, Oisin Mac and Julier, Simon and Brostow, Gabriel J.},
doi = {10.1109/CVPR.2016.586},
file = {:Users/hanryham/Downloads/depth{\_}prediction{\_}main.pdf:pdf},
isbn = {978-1-4673-8851-1},
issn = {10636919},
journal = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
mendeley-groups = {3D Reconstruction},
pages = {5431--5440},
title = {{Structured Prediction of Unobserved Voxels from a Single Depth Image}},
url = {http://ieeexplore.ieee.org/document/7780955/},
year = {2016}
}
@article{Wu2016,
abstract = {Understanding 3D object structure from a single image is an important but difficult task in computer vision, mostly due to the lack of 3D object annotations in real images. Previous work tackles this problem by either solving an optimization task given 2D keypoint positions, or training on synthetic data with ground truth 3D information. In this work, we propose 3D INterpreter Network (3D-INN), an end-to-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, trained on both real 2D-annotated images and synthetic 3D data. This is made possible mainly by two technical innovations. First, we propose a Projection Layer, which projects estimated 3D structure to 2D space, so that 3D-INN can be trained to predict 3D structural parameters supervised by 2D annotations on real images. Second, heatmaps of keypoints serve as an intermediate representation connecting real and synthetic data, enabling 3D-INN to benefit from the variation and abundance of synthetic 3D objects, without suffering from the difference between the statistics of real and synthesized images due to imperfect rendering. The network achieves state-of-the-art performance on both 2D keypoint estimation and 3D structure recovery. We also show that the recovered 3D information can be used in other vision applications, such as 3D rendering and image retrieval.},
archivePrefix = {arXiv},
arxivId = {1604.08685},
author = {Wu, Jiajun and Xue, Tianfan and Lim, Joseph J. and Tian, Yuandong and Tenenbaum, Joshua B. and Torralba, Antonio and Freeman, William T.},
doi = {10.1007/978-3-319-46466-4_22},
eprint = {1604.08685},
file = {:Users/hanryham/Downloads/1604.08685.pdf:pdf},
isbn = {9783319464657},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {3D structure,Keypoint estimation,Neural network,Single image 3D reconstruction,Synthetic data},
mendeley-groups = {3D Reconstruction},
pages = {365--382},
pmid = {4520227},
title = {{Single image 3D interpreter network}},
volume = {9910 LNCS},
year = {2016}
}
@article{Rezende2016,
abstract = {A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods on several datasets, including ShapeNet [2], and establish the first benchmarks in the literature. We also show how these models and their inference networks can be trained end-to-end from 2D images. This demonstrates for the first time the feasibility of learning to infer 3D representations of the world in a purely unsupervised manner.},
archivePrefix = {arXiv},
arxivId = {1607.00662},
author = {Rezende, Danilo Jimenez and Eslami, S. M. Ali and Mohamed, Shakir and Battaglia, Peter and Jaderberg, Max and Heess, Nicolas},
eprint = {1607.00662},
file = {:Users/hanryham/Downloads/1607.00662.pdf:pdf},
issn = {10495258},
mendeley-groups = {3D Reconstruction},
title = {{Unsupervised Learning of 3D Structure from Images}},
url = {http://arxiv.org/abs/1607.00662},
year = {2016}
}
@article{Plyer2016,
abstract = {This paper deals with dense optical flow estimation from the perspective of the trade-off between quality of the estimated flow and computational cost which is required by real-world applications. We propose a fast and robust local method, denoted by eFOLKI, and describe its implementation on GPU. It leads to very high performance even on large image formats such as 4 K (3,840 × 2,160) resolution. In order to assess the interest of eFOLKI, we first present a comparative study with currently available GPU codes, including local and global methods, on a large set of data with ground truth. eFOLKI appears significantly faster while providing quite accurate and highly robust estimated flows. We then show, on four real-time video processing applications based on optical flow, that eFOLKI reaches the requirements both in terms of estimated flows quality and of processing rate.},
author = {Plyer, Aur{\'{e}}lien and {Le Besnerais}, Guy and Champagnat, Fr{\'{e}}d{\'{e}}ric},
doi = {10.1007/s11554-014-0423-0},
issn = {18618200},
journal = {Journal of Real-Time Image Processing},
keywords = {Dense,GPU,Motion,Optical flow,Super-resolution},
mendeley-groups = {3D Reconstruction},
number = {4},
pages = {713--730},
title = {{Massively parallel Lucas Kanade optical flow for real-time video processing applications}},
volume = {11},
year = {2016}
}
@article{Tsiafaki2015,
abstract = {The application of 3D digital technologies in the archaeological research expands more and more during the last decades. 3D recording, visualisation, representation and reconstruction of archaeological sites, monu-ments and artefacts become almost a common trend in the archaeological work. Moreover, CAD reconstruc-tions, 3D simulation, computer animation and other uses of computer systems change the traditional work-flow. The archaeological experience, though, recognizes these tools more for the general public in order to offer a visualisation of the target object than as a mechanism that can offer new possibilities for the research itself. The focus of this article is to explore the opportunities given to the research field of archaeology by 3D tech-nologies. Specifically the article will look into technologies regarding the 3D recording, processing, visualisa-tion and representation of archaeological data. Through the use of specific case studies we will investigate how applications can contribute to the understanding first and the interpretation later on of a certain ar-chaeological object. Issues such as the types of questions and problems that can be faced and answered with these 3D technologies will be raised and discussed. The possibility to get an expansion of the archaeological research in new aspects, as it happens with other technological tools (e.g. databases), will be also examined. In addition to these, the disadvantages and limitations of the application of these 3D technologies in the ar-chaeological field will be also looked at, in order to accomplish a more complete view of its usage.},
annote = {NULL},
author = {Tsiafaki, Despoina and Michailidou, Natasa},
doi = {10.5281/zenodo.18448},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Tsiafaki, Michailidou - 2015 - Benefits and Problems Through the Application of 3D Technologies in Archaeology Recording, Visualisation,.pdf:pdf},
journal = {SCIENTIFIC CULTURE Tsiafaki {\&} Michailidou SCIENTIFIC CULTURE},
keywords = {3D technologies,archaeology,benefits,problems,reconstruction,recording,representation,visualisation},
mendeley-groups = {3D Reconstruction},
number = {3},
pages = {37--45},
title = {{Benefits and Problems Through the Application of 3D Technologies in Archaeology: Recording, Visualisation, Representation and Reconstruction}},
volume = {1},
year = {2015}
}
@article{Cao2015,
abstract = {We present the first real-time high-fidelity facial capture method. The core idea is to enhance a global real-time face tracker, which provides a low-resolution face mesh, with local regressors that add in medium-scale details, such as expression wrinkles. Our main observation is that although wrinkles appear in different scales and at different locations on the face, they are locally very self-similar and their visual appearance is a direct consequence of their local shape. We therefore train local regressors from high-resolution capture data in order to predict the local geometry from local appearance at runtime. We propose an automatic way to detect and align the local patches required to train the regressors and run them efficiently in real-time. Our formulation is particularly designed to enhance the low-resolution global tracker with exactly the missing expression frequencies, avoiding superimposing spatial frequencies in the result. Our system is generic and can be applied to any real-time tracker that uses a global prior, e.g. blend-shapes. Once trained, our online capture approach can be applied to any new user without additional training, resulting in high-fidelity facial performance reconstruction with person-specific wrinkle details from a monocular video camera in real-time.},
annote = {NULL},
author = {Cao, Chen and Bradley, Derek},
doi = {10.1145/2766943},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Cao, Bradley - 2015 - Real-Time High-Fidelity Facial Performance Capture.pdf:pdf},
isbn = {9781450333313},
issn = {07300301},
journal = {ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH 2015},
keywords = {Real-time face reconstruction,high-fidelity performance capture,local wrinkle model,real-time face reconstruction},
mendeley-groups = {3D Reconstruction},
number = {4},
pages = {1--9},
title = {{Real-Time High-Fidelity Facial Performance Capture}},
volume = {34},
year = {2015}
}
@article{Evangelidis2015,
abstract = {This paper addresses the problem of range-stereo fusion, for the construction of high-resolution depth maps. In particular, we combine low-resolution depth data with high-resolution stereo data, in a maximum a posteriori (MAP) formulation. Unlike existing schemes that build on MRF optimizers, we infer the disparity map from a series of local energy minimization problems that are solved hierarchically, by growing sparse initial disparities obtained from the depth data. The accuracy of the method is not compromised, owing to three properties of the data-term in the energy function. First, it incorporates a new correlation function that is capable of providing refined correlations and disparities, via subpixel correction. Second, the correlation scores rely on an adaptive cost aggregation step, based on the depth data. Third, the stereo and depth likelihoods are adaptively fused, based on the scene texture and camera geometry. These properties lead to a more selective growing process which, unlike previous seed-growing methods, avoids the tendency to propagate incorrect disparities. The proposed method gives rise to an intrinsically efficient algorithm, which runs at 3FPS on 2.0 MP images on a standard desktop computer. The strong performance of the new method is established both by quantitative comparisons with state-of-the-art methods, and by qualitative comparisons using real depth-stereo data-sets.},
annote = {Evangelidis et al. introduced a fusion of range and stereo data for high-resolution scene-modelling. They combine low-resolution depth data with high resolution stereo data. They stated that the accuracy of proposed method is not comprimised. They used a maximum posteriori (MAP) formulation. These properties lead to a more selective growing process as known as seed-growing manner with their own preset.},
author = {Evangelidis, Georgios D. and Hansard, Miles and Horaud, Radu},
doi = {10.1109/TPAMI.2015.2400465},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Evangelidis, Hansard, Horaud - 2015 - Fusion of Range and Stereo Data for High-Resolution Scene-Modeling.pdf:pdf},
isbn = {0162-8828},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Stereo,maximum a posteriori,range data,seed-growing,sensor fusion,time-of-flight camera},
mendeley-groups = {3D Reconstruction},
number = {11},
pages = {2178--2192},
pmid = {26440260},
title = {{Fusion of Range and Stereo Data for High-Resolution Scene-Modeling}},
volume = {37},
year = {2015}
}
@article{Santoso2015,
abstract = {In this review, we discuss state-of-the-art developments in 3D models for small and rigid structures. This includes the pros and cons of cutting-edge range cameras used as active 3D scanners, while also considering passive image reconstruction schemes by means of the well-known “Structure-from-Motion” (SfM) algorithms. Furthermore, we discuss the issue of how data fusion algorithms can be used to optimally fuse 2D contour information onto 3D models for several different applications. Considering the benefits of 3D range sensors, we also review current trends in optimum data fusion of point clouds from 3D range sensors. We present the benefits and the limitations of each algorithm against various design considerations. To highlight the pros and cons, we also perform a comparative study of the performance of a 3D range sensor, represented by an iPad structure sensor, with respect to the well-known structure-from-motion software packages, namely, Bundler, Microsoft PhotoSynth, Agisoft PhotoScan and Smart3DCapture. Lastly, we highlight several research opportunities and potential research challenges associated with each technique.},
author = {Santoso, Fendy and Garratt, Matthew and Pickering, Mark and Asikuzzaman, Md},
doi = {10.1109/JSEN.2015.2498940},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Santoso et al. - 2015 - 3D-Mapping for Visualisation of Rigid Structures A Review and Comparative Study.pdf:pdf},
issn = {1530-437X},
journal = {IEEE Sensors Journal},
keywords = {3D Modelling,Accuracy,Cameras,Data Fusion,Laser radar,Point Clouds,Range Camera,Sensors,Solid modeling,Structure-from-Motion (SfM),Tablet computers,Three-dimensional displays},
mendeley-groups = {3D Reconstruction},
number = {99},
pages = {1--1},
title = {{3D-Mapping for Visualisation of Rigid Structures: A Review and Comparative Study}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber =7322186},
volume = {PP},
year = {2015}
}
@article{Sarbolandi2015,
abstract = {Recently, the new Kinect One has been issued by Microsoft, providing the next generation of real-time range sensing devices based on the Time-of-Flight (ToF) principle. As the first Kinect version was using a structured light approach, one would expect various differences in the characteristics of the range data delivered by both devices. This paper presents a detailed and in-depth comparison between both devices. In order to conduct the comparison, we propose a framework of seven different experimental setups, which is a generic basis for evaluating range cameras such as Kinect. The experiments have been designed with the goal to capture individual effects of the Kinect devices as isolatedly as possible and in a way, that they can also be adopted, in order to apply them to any other range sensing device. The overall goal of this paper is to provide a solid insight into the pros and cons of either device. Thus, scientists who are interested in using Kinect range sensing cameras in their specific application scenario can directly assess the expected, specific benefits and potential problem of either device.},
annote = {NULL},
archivePrefix = {arXiv},
arxivId = {arXiv:1505.05459v1},
author = {Sarbolandi, Hamed and Lefloch, Damien and Kolb, Andreas},
doi = {10.1016/j.cviu.2015.05.006},
eprint = {arXiv:1505.05459v1},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Sarbolandi, Lefloch, Kolb - 2015 - Kinect range sensing Structured-light versus Time-of-Flight Kinect.pdf:pdf},
isbn = {1077-3142},
issn = {1090235X},
journal = {Computer Vision and Image Understanding},
keywords = {3D,Depth sensor,Evaluation,Kinect},
mendeley-groups = {3D Reconstruction},
pages = {1--20},
title = {{Kinect range sensing: Structured-light versus Time-of-Flight Kinect}},
volume = {139},
year = {2015}
}
@article{Bernadin2015,
author = {Bernadin, Shonda L. and Tharpe, Aubrey and Wagner, Taylor and Daupin, Rachelle and Cardenas, Nick},
doi = {10.1109/SECON.2015.7132878},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Bernadin et al. - 2015 - Work-in-progress Designing a 3D scanner for digital reconstruction of rare cultural artifacts.pdf:pdf},
isbn = {9781467373005},
issn = {07347502},
journal = {Conference Proceedings - IEEE SOUTHEASTCON},
keywords = {3D image reconstruction,3D scanning,digital humanities,digital photogrammetry},
mendeley-groups = {3D Reconstruction},
number = {June},
pages = {3--4},
title = {{Work-in-progress: Designing a 3D scanner for digital reconstruction of rare cultural artifacts}},
volume = {2015-June},
year = {2015}
}
@article{Ruiz-Sarmiento2015,
abstract = {{\textcopyright} 2015 IEEE.In this work we present the Object Labeling Toolkit (OLT), a set of software components publicly available for helping in the management and labeling of sequential RGB-D observations collected by a mobile robot. Such a robot can be equipped with an arbitrary number of RGB-D devices, possibly integrating other sensors (e.g. odometry, 2D laser scanners, etc.). OLT first merges the robot observations to generate a 3D reconstruction of the scene from which object segmentation and labeling is conveniently accomplished. The annotated labels are automatically propagated by the toolkit to each RGB-D observation in the collected sequence, providing a dense labeling of both intensity and depth images. The resulting objects' labels can be exploited for many robotic oriented applications, including high-level decision making, semantic mapping, or contextual object recognition. Software components within OLT are highly customizable and expandable, facilitating the integration of already-developed algorithms. To illustrate the toolkit suitability, we describe its application to robotic RGB-D sequences taken in a home environment.},
author = {Ruiz-Sarmiento, J. R. and Galindo, C. and Gonzalez-Jimenez, J.},
doi = {10.1109/ECMR.2015.7324214},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Ruiz-Sarmiento, Galindo, Gonzalez-Jimenez - 2015 - OLT A Toolkit for Object Labeling applied to robotic RGB-D datasets.pdf:pdf},
isbn = {9781467391634},
journal = {2015 European Conference on Mobile Robots, ECMR 2015 - Proceedings},
keywords = {Image reconstruction,Labeling,Robot kinematics,Robot sensing systems,Three-dimensional displays},
mendeley-groups = {3D Reconstruction},
title = {{OLT: A Toolkit for Object Labeling applied to robotic RGB-D datasets}},
year = {2015}
}
@article{Kowalski2015,
abstract = {Abstract LiveScan3D is a free, open source system for live, 3D data acquisition using multiple Kinect v2 sensors. It allows the user to place any number of sensors in any physical configuration and start gathering data at real time speed. The freedom of placing the ...$\backslash$n},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Kowalski, Marek and Naruniec, Jacek and Daniluk, Micha},
doi = {10.1109/3DV.2015.43},
eprint = {arXiv:1011.1669v3},
file = {:Users/hanryham/Downloads/07335499.pdf:pdf},
isbn = {9781467383325},
issn = {978-1-4673-8332-5},
journal = {Proceedings - 2015 International Conference on 3D Vision, 3DV 2015},
keywords = {3D reconstruction,Kinect,Live Scan3D,open source},
mendeley-groups = {3D Reconstruction},
pages = {318--325},
pmid = {26528727},
title = {{Live Scan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors}},
year = {2015}
}
@article{Chang2015,
abstract = {We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Annotations are made available through a public web-based interface to enable data visualization of object attributes, promote data-driven geometric analysis, and provide a large-scale quantitative benchmark for research in computer graphics and vision. At the time of this technical report, ShapeNet has indexed more than 3,000,000 models, 220,000 models out of which are classified into 3,135 categories (WordNet synsets). In this report we describe the ShapeNet effort as a whole, provide details for all currently available datasets, and summarize future plans.},
archivePrefix = {arXiv},
arxivId = {1512.03012},
author = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
doi = {10.1145/3005274.3005291},
eprint = {1512.03012},
file = {:Users/hanryham/Downloads/1512.03012.pdf:pdf},
isbn = {9781450345408},
mendeley-groups = {3D Reconstruction},
title = {{ShapeNet: An Information-Rich 3D Model Repository}},
url = {http://arxiv.org/abs/1512.03012},
year = {2015}
}
@article{Nadu2015,
author = {Nadu, Tamil},
file = {:Users/hanryham/Downloads/IJAER{\_}v10{\_}n76{\_}pp427-432{\_}oct2015.pdf:pdf},
keywords = {clusterin,fuzzy c-means,seeded region growing,segmentation,tumor},
mendeley-groups = {3D Reconstruction},
number = {76},
pages = {427--432},
title = {{Brain Tumor Segmentation of MRI Brain Images through FCM clustering and Seeded Region Growing Technique}},
volume = {10},
year = {2015}
}
@article{Jaiswal2014,
abstract = {{\textcopyright} 2014 Asia-Pacific Signal and Information Processing Ass.RGB-D (Kinect-style) cameras are novel low-cost sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate the use of such cameras for acquiring multiple images of an object from multiple viewpoints and building complete 3D models of objects. Such models have applications in a wide range of industries. We implemented a complete 3D object model construction process with object segmentation, registration, global alignment, model denoising, and texturing, and studied the effects of these functions on the constructed 3D object models. We also developed a process for objective performance evaluation of the constructed 3D object models. We collected laser scan data as the ground truth using a Roland Pieza LPX-600 Laser Scanner to compare to the 3D models created by our process.},
annote = {3D object modelling is the process of developing a mathematical representation of the three dimensional surface of an object. 3D object reconstruction can be accomplished by connecting all the collection of points in the 3D space, as known as point cloud.

This research uses kinect camera for acquiring RGB and depth images of small object from limited number of viewpoints and building a complete 3D model of the object. They introduces their pipeline as follows: 
1. fine registration (Sift descriptor of each 3d point, Ransac to eliminate missed allignement on matched 3d point, icp), subsequently 
2. global alignment to eliminate innacuracy at each registration steps
3. Model de-noising
is used to refine the 3D object model using Moving Least Square (MLS)
4. Surface representation
applied triangulate method of point clouds to build a mesh surface and then map the RGB color from the RGB image to the mesh. Subsequently, they assign color to each vertex and apllied interpolate the color in each triangle faces.
5. 3D Model

Conts: They set a scenario of acquisition step using a green screen and implement a simple threshold to segment out object. Afterwards, the foreground object mask is then applied to the depth image.},
author = {Jaiswal, Mayoore and Xie, Jun and Sun, Ming Ting},
doi = {10.1109/APSIPA.2014.7041821},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Jaiswal, Xie, Sun - 2014 - 3D object modeling with a Kinect camera.pdf:pdf},
isbn = {9786163618238},
journal = {2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014},
mendeley-groups = {3D Reconstruction},
title = {{3D object modeling with a Kinect camera}},
year = {2014}
}
@article{Lim2014,
annote = {NULL},
author = {Lim, Seng Poh and Haron, Habibollah},
doi = {10.1007/s10462-012-9329-z},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Lim, Haron - 2014 - Surface reconstruction techniques A review.pdf:pdf},
issn = {02692821},
journal = {Artificial Intelligence Review},
keywords = {Soft computing methods,Surface reconstruction,Unstructured points},
mendeley-groups = {3D Reconstruction},
number = {1},
pages = {59--78},
title = {{Surface reconstruction techniques: A review}},
volume = {42},
year = {2014}
}
@article{Chang2014,
author = {Chang, Jiho and Jeong, Jae-chan and Hwang, Dae-Hwan},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Chang, Jeong, Hwang - 2014 - Real-time Hybrid Stereo Vision System for HD resolution disparity map.pdf:pdf},
journal = {Proceedings of the British Machine Vision Conference},
mendeley-groups = {3D Reconstruction},
pages = {1--11},
title = {{Real-time Hybrid Stereo Vision System for HD resolution disparity map}},
year = {2014}
}
@article{Hao2013,
abstract = {3D model-based object recognition has been a noticeable research trend in recent years. Common methods find 2D-to-3D correspondences and make recognition decisions by pose estimation, whose efficiency usually suffers from noisy correspondences caused...},
annote = {NULL},
author = {Hao, Qiang and Cai, Rui and Li, Zhiwei and Zhang, Lei and Pang, Yanwei and Wu, Feng and Rui, Yong},
doi = {10.1109/CVPR.2013.121},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Hao et al. - 2013 - Efficient 2D-to-3D correspondence filtering for scalable 3D object recognition.pdf:pdf},
isbn = {978-0-7695-4989-7},
issn = {10636919},
journal = {Proceedings of the IEEE Computer Society Conference on Computer  Vision and Pattern Recognition},
mendeley-groups = {3D Reconstruction},
number = {1},
pages = {899--906},
title = {{Efficient 2D-to-3D correspondence filtering for scalable 3D object recognition}},
year = {2013}
}
@inproceedings{Sobani2013,
annote = {NULL},
author = {Sobani, Siti Syazalina Mohd and Daud, Siti Asmah and Mahmood, Nasrul Humaimi},
booktitle = {2013 1st International Conference on Artificial Intelligence, Modelling and Simulation},
doi = {10.1109/AIMS.2013.52},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Sobani, Daud, Mahmood - 2013 - Implementation of 3D Object Reconstruction Using Radon Transform.pdf:pdf},
isbn = {978-1-4799-3251-1},
mendeley-groups = {3D Reconstruction},
month = {dec},
pages = {284--288},
publisher = {IEEE},
title = {{Implementation of 3D Object Reconstruction Using Radon Transform}},
url = {http://ieeexplore.ieee.org/document/6959930/},
year = {2013}
}
@article{Leonardi2013,
annote = {NULL},
author = {Leonardi, Valentin and Vidal, Vincent and Mari, Jean-Luc and Daniel, Marc},
doi = {10.1145/2461217.2461239},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Leonardi et al. - 2013 - 3D Reconstruction from CT-scan Volume Dataset Application to Kidney Modeling.pdf:pdf},
isbn = {978-1-4503-1978-2},
journal = {Proceedings of the 27th Spring Conference on Computer Graphics},
keywords = {geometrical modeling,mesh,reconstruction,region growing,segmentation},
mendeley-groups = {3D Reconstruction},
pages = {111--120},
title = {{3D Reconstruction from CT-scan Volume Dataset Application to Kidney Modeling}},
url = {http://doi.acm.org/10.1145/2461217.2461239},
volume = {D},
year = {2013}
}
@article{Keller2013,
abstract = {Real-time or online 3D reconstruction has wide applicability and receives further interest due to availability of consumer depth cameras. Typical approaches use a moving sensor to accumulate depth measurements into a single model which is continuously refined. Designing such systems is an intricate balance between reconstruction quality, speed, spatial scale, and scene assumptions. Existing online methods either trade scale to achieve higher quality reconstructions of small objects/scenes. Or handle larger scenes by trading real-time performance and/or quality, or by limiting the bounds of the active reconstruction. Additionally, many systems assume a static scene, and cannot robustly handle scene motion or reconstructions that evolve to reflect scene changes. We address these limitations with a new system for real-time dense reconstruction with equivalent quality to existing online methods, but with support for additional spatial scale and robustness in dynamic scenes. Our system is designed around a simple and flat point-Based representation, which directly works with the input acquired from range/depth sensors, without the overhead of converting between representations. The use of points enables speed and memory efficiency, directly leveraging the standard graphics pipeline for all central operations, i.e., camera pose estimation, data association, outlier removal, fusion of depth maps into a single denoised model, and detection and update of dynamic objects. We conclude with qualitative and quantitative results that highlight robust tracking and high quality reconstructions of a diverse set of scenes at varying scales.},
author = {Keller, Maik and Lefloch, Damien and Lambers, Martin and Izadi, Shahram and Weyrich, Tim and Kolb, Andreas},
doi = {10.1109/3DV.2013.9},
file = {:Users/hanryham/Downloads/06599048.pdf:pdf},
isbn = {978-0-7695-5067-1},
journal = {2013 International Conference on 3D Vision},
mendeley-groups = {3D Reconstruction},
pages = {1--8},
title = {{Real-Time 3D Reconstruction in Dynamic Scenes Using Point-Based Fusion}},
url = {http://ieeexplore.ieee.org/document/6599048/},
year = {2013}
}
@article{Doumanoglou2013,
abstract = {Hereby, a new publicly available 3D reconstruction-oriented dataset is presented. It consists of multi-view range scans of small-sized objects using a turntable. Range scans were captured using a Microsoft Kinect sensor, as well as an accurate laser scanner (Vivid VI-700 Non-contact 3D Digitizer), whose reconstructions can serve as ground-truth data. The construction of this dataset was motivated by the lack of a relevant Kinect dataset, despite the fact that Kinect has attracted the attention of many researchers and home enthusiasts. Thus, the core idea behind the construction of this dataset, is to allow the validation of 3D surface reconstruction methodologies for point sets extracted using Kinect sensors. The dataset consists of multi-view range scans of 59 objects, along with the necessary calibration information that can be used for experimentation in the field of 3D reconstruction from Kinect depth data. Two well-known 3D reconstruction methods were selected and applied on the dataset, in order to demonstrate its applicability in the 3D reconstruction field, as well as the challenges that arise. Additionally, the appropriate 3D reconstruction evaluation methodology is presented. Finally, as the dataset comes in classes of similar objects, it can also be used for classification purposes, using the provided 2.5D/3D features.},
author = {Doumanoglou, Alexandros and Asteriadis, Stylianos and Alexiadis, Dimitrios S. and Zarpalas, Dimitrios and Daras, Petros},
doi = {10.1109/IVMSPW.2013.6611937},
file = {:Users/hanryham/Downloads/06611937.pdf:pdf},
isbn = {9781467358583},
issn = {978-1-4673-5858-3},
journal = {2013 IEEE 11th IVMSP Workshop: 3D Image/Video Technologies and Applications, IVMSP 2013 - Proceedings},
keywords = {3D reconstruction dataset,Fourier-based 3D reconstruction,Kinect Sensor,Poisson surface reconstruction,Vivid VI-700 Non-contact 3D Digitizer},
mendeley-groups = {3D Reconstruction},
title = {{A dataset of Kinect-based 3D scans}},
year = {2013}
}
@article{Yan2013,
abstract = {Speeded-Up Robust Feature (SURF) algorithm is widely used for image feature detecting and matching in computer vision area. Open Computing Language (OpenCL) is a framework for writing programs that execute across heterogeneous platforms consisting of CPUs, GPUs, and other processors. This paper introduces how to implement an open-sourced SURF program, namely OpenSURF, on general purpose GPU by OpenCL, and discusses the optimizations in terms of the thread architectures and memory models in detail. Our final OpenCL implementation of OpenSURF is on average 37{\%} and 64{\%} faster than the OpenCV SURF v2.4.5 CUDA implementation on NVidia's GTX660 and GTX460SE GPUs, repectively. Our OpenCL program achieved real-time performance ({\textgreater}25 Frames Per Second) for almost all the input images with different sizes from 320*240 to 1024*768 on NVidia's GTX660 GPU, NVidia's GTX460SE GPU and AMD's Radeon HD 6850 GPU. Our OpenCL approach on NVidia's GTX660 GPU is more than 22.8 times faster than its original CPU version on Intel's Dual-Core E5400 2.7G on average.},
author = {Yan, Wanglong and Shi, Xiaohua and Yan, Xin and Wang, Lina},
doi = {10.5772/57057},
file = {:Users/hanryham/Downloads/57057.pdf:pdf},
issn = {17298806},
journal = {International Journal of Advanced Robotic Systems},
keywords = {General purpose GPU,OpenCL,OpenSURF},
mendeley-groups = {3D Reconstruction},
pages = {1--12},
title = {{Computing OpenSURF on OpenCL and general purpose GPU}},
volume = {10},
year = {2013}
}
@article{Xie2013,
author = {Xie, Jun and Hsu, YF and Feris, RS and Sun, MT},
file = {:Users/hanryham/Downloads/a74378d2645cf022dc9e7206846b7a168b9d.pdf:pdf},
journal = {Circuits and Systems (ISCAS {\ldots}},
mendeley-groups = {3D Reconstruction},
pages = {1--4},
title = {{Fine registration of 3D point clouds with iterative closest point using an RGB-D camera}},
url = {http://staff.washington.edu/junx/publication/Fine Registration{\_}ISCAS13.pdf{\%}5Cnhttp://ieeexplore.ieee.org/xpls/abs {\_}all.jsp?arnumber=6572486},
year = {2013}
}
@article{Dahan2012,
abstract = {As depth cameras become more popular, pixel depth information becomes easier to obtain. This information can clearly enhance many image processing applications. However, combining depth and color information is not straightforward as these two signals can have different noise characteristics, differences in resolution, and their boundaries do not generally agree. We present a technique that combines depth and color image information from real devices in synergy. In particular, we focus on combining them to improve image segmentation. We use color information to fill and clean depth and use depth to enhance color image segmentation. We demonstrate the utility of the combined segmentation for extracting layers and present a novel image retargeting algorithm for layered images.},
author = {Dahan, Meir Johnathan and Chen, Nir and Shamir, Ariel and Cohen-Or, Daniel},
doi = {10.1007/s00371-011-0667-7},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Dahan et al. - 2012 - Combining color and depth for enhanced image segmentation and retargeting.pdf:pdf},
issn = {01782789},
journal = {Visual Computer},
keywords = {Depth maps,Image segmentation,Retargeting},
mendeley-groups = {Saliency Detection,3D Reconstruction},
number = {12},
pages = {1181--1193},
title = {{Combining color and depth for enhanced image segmentation and retargeting}},
volume = {28},
year = {2012}
}
@article{Depth2012,
author = {Depth, Joint and Calibration, Color Camera},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Depth, Calibration - 2012 - Short Papers {\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}.pdf:pdf},
mendeley-groups = {3D Reconstruction},
number = {10},
pages = {2058--2064},
title = {{Short Papers {\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}}},
volume = {34},
year = {2012}
}
@article{Cui2012,
abstract = {We present a novel scanning system for capturing a full 3D human body model using just a single depth camera and no auxiliary equipment. We claim that data captured from a single Kinect is sufficient to produce a good qual-ity full 3D human model. In this setting, the challenges we face are the sensor's low resolution with random noise and the subject's non-rigid movement when capturing the data. To overcome these challenges, we develop an improved super-resolution algorithm that takes color constraints into account. We then align the super-resolved scans using a combination of automatic rigid and non-rigid reg-istration. As the system is of low price and obtains impressive results in several minutes, full 3D human body scanning technology can now become more acces-sible to everyday users at home.},
annote = {NULL},
author = {Cui, Yan and Chang, Will and Tobias, N},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Cui, Chang, Tobias - 2012 - KinectAvatarFully Automatic Body Capture Using a Single Kinect.pdf:pdf},
journal = {ACCV Workshops},
mendeley-groups = {3D Reconstruction},
title = {{KinectAvatar:Fully Automatic Body Capture Using a Single Kinect}},
year = {2012}
}
@article{Vieira2012,
annote = {Viera et al. said that most methods rely on the minimization of the matching error emong individual depth frames. Apart of that, ambiguity in sensor data leads to a problem during the matching process. Therefore, they implement semi-automatic approach which allows user to translate and rotate individual RGB-D point clouds along the degrees of freedom with given interface. Subsequently, 3d point clouds were aligned using Iterative closes points (ICP).
This approach shows a better result compared to raw RGB-D data. However, smaller 3D environment was evaluated in their research.},
author = {Vieira, Pedro},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Vieira - 2012 - Interactive 3D Scan-Matching Using RGB-D Data ∗.pdf:pdf},
isbn = {9781467347372},
mendeley-groups = {3D Reconstruction},
pages = {0--3},
title = {{Interactive 3D Scan-Matching Using RGB-D Data ∗}},
year = {2012}
}
@article{Alexiadis2012,
author = {Alexiadis, D S and Kordelas, G and Apostolakis, K C and Agapito, J D and Vegas, J M and Izquierdo, E and Daras, P},
file = {:Users/hanryham/Downloads/06226760.pdf:pdf},
journal = {Image Analysis for Multimedia Interactive Services (WIAMIS), 2012 13th International Workshop},
mendeley-groups = {3D Reconstruction},
pages = {1--4},
title = {{Reconstruction for 3D immersive virtual environments}},
year = {2012}
}
@article{Nguyen2012,
abstract = {We contribute an empirically derived noise model for the Kinect sensor. We systematically measure both lateral and axial noise distributions, as a function of both distance and angle of the Kinect to an observed surface. The derived noise model can be used to filter Kinect depth maps for a variety of applications. Our second contribution applies our derived noise model to the KinectFusion system to extend filtering, volumetric fusion, and pose estimation within the pipeline. Qualitative results show our method allows reconstruction of finer details and the ability to reconstruct smaller objects and thinner surfaces. Quantitative results also show our method improves pose estimation accuracy.},
archivePrefix = {arXiv},
arxivId = {arXiv:1505.0193},
author = {Nguyen, Chuong V. and Izadi, Shahram and Lovell, David},
doi = {10.1109/3DIMPVT.2012.84},
eprint = {arXiv:1505.0193},
file = {:Users/hanryham/Downloads/06375037.pdf:pdf},
isbn = {9780769548739},
issn = {978-1-4673-4470-8},
journal = {Proceedings - 2nd Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization and Transmission, 3DIMPVT 2012},
mendeley-groups = {3D Reconstruction},
pages = {524--530},
pmid = {4650729},
title = {{Modeling kinect sensor noise for improved 3D reconstruction and tracking}},
year = {2012}
}
@article{242011,
abstract = {We analyze Kinect as a 3D measuring device, experimen- tally investigate depth measurement resolution and error properties and make a quantitative comparison of Kinect accuracy with stereo reconstruction from SLR cameras and a 3D-TOF camera. We propose Kinect geometrical model and its calibration procedure providing an accurate cali- bration of Kinect 3D measurement and Kinect cameras. We demonstrate the functionality of Kinect calibration by in- tegrating it into an SfM pipeline where 3D measurements from a moving Kinect are transformed into a common coor- dinate system by computing relative poses from matches in color camera.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {24, Paper},
doi = {10.1109/ICCVW.2011.6130380},
eprint = {arXiv:1011.1669v3},
file = {:Users/hanryham/Downloads/06130380.pdf:pdf},
isbn = {9781467300636},
issn = {978-1-4673-0063-6},
journal = {Image (Rochester, N.Y.)},
mendeley-groups = {3D Reconstruction},
pages = {1154--1160},
pmid = {9781447146391},
title = {{3D with Kinect}},
year = {2011}
}
@article{Avron2010,
abstract = {We introduce an l1-sparse method for the reconstruction of a piecewise smooth point set surface. The technique is motivated by recent advancements in sparse signal reconstruction. The assumption underlying our work is that common objects, even geometrically complex ones, can typically be characterized by a rather small number of features. This, in turn, naturally lends itself to incorporating the powerful notion of sparsity into the model. The sparse reconstruction principle gives rise to a reconstructed point set surface that consists mainly of smooth modes, with the residual of the objective function strongly concentrated near sharp features. Our technique is capable of recovering orientation and positions of highly noisy point sets. The global nature of the optimization yields a sparse solution and avoids local minima. Using an interior-point log-barrier solver with a customized preconditioning scheme, the solver for the corresponding convex optimization problem is competitive and the results are of high quality.},
author = {Avron, Haim and Sharf, Andrei and Greif, Chen and Cohen-Or, Daniel},
doi = {10.1145/1857907.1857911},
file = {:Users/hanryham/Downloads/l1sparse-tog-1.15.pdf:pdf},
isbn = {0730-0301},
issn = {07300301},
journal = {ACM Transactions on Graphics},
mendeley-groups = {3D Reconstruction},
number = {5},
pages = {1--12},
title = {{ℓ {\textless}sub{\textgreater}1{\textless}/sub{\textgreater} -Sparse reconstruction of sharp point set surfaces}},
url = {http://portal.acm.org/citation.cfm?doid=1857907.1857911},
volume = {29},
year = {2010}
}
@article{Riemens2009,
abstract = {Depth maps are used in many applications, e.g. 3D television, stereo matching, segmentation, etc. Often, depth maps are available at a lower resolution compared to the corresponding image data. For these applications, depth maps must be upsampled to the image resolution. Recently, joint bilateral filters are proposed to upsample depth maps in a single step. In this solution, a high-resolution output depth is computed as a weighted average of surrounding low-resolution depth values, where the weight calculation depends on spatial distance function and intensity range function on the related image data. Compared to that, we present two novel ideas. Firstly, we apply anti-alias prefiltering on the high-resolution image to derive an image at the same low resolution as the input depth map. The upsample filter uses samples from both the high-resolution and the low-resolution images in the range term of the bilateral filter. Secondly, we propose to perform the upsampling in multiple stages, refining the resolution by a factor of 2×2 at each stage. We show experimental results on the consequences of the aliasing issue, and we apply our method to two use cases: a high quality ground-truth depth map and a real-time generated depth map of lower quality. For the first use case a relatively small filter footprint is applied; the second use case benefits from a substantially larger footprint. These experiments show that the dual image resolution range function alleviates the aliasing artifacts and therefore improves the temporal stability of the output depth map. On both use cases, we achieved comparable or better image quality with respect to upsampling with the joint bilateral filter in a single step. On the former use case, we feature a reduction of a factor of 5 in computational cost, whereas on the latter use case, the cost saving is a factor of 50.},
author = {Riemens, A. K. and Gangwal, O. P. and Barenbrug, B. and Berretty, R.-P. M. R.-PM},
doi = {10.1117/12.805640},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Riemens et al. - 2009 - Multi-step joint bilateral depth upsampling.pdf:pdf},
issn = {0277786X},
journal = {IS{\&}T/SPIE Electronic Imaging},
keywords = {3d tv,bilateral filters,depth estimation,depth map,disparity,disparity map,nonlinear filters,scaling},
mendeley-groups = {3D Reconstruction},
pages = {72570M--72570M--12},
title = {{Multi-step joint bilateral depth upsampling}},
url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1335208},
volume = {7257},
year = {2009}
}
@article{Juan2009,
abstract = {This paper summarizes the three robust feature detection methods: Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)SIFT and Speeded Up Robust Features (SURF). This paper uses KNN (K-Nearest Neighbor) and Random Sample Consensus (RANSAC) to the three methods in order to analyze the results of the methods application in recognition. KNN is used to find the matches, and RANSAC to reject inconsistent matches from which the inliers can take as correct matches. The performance of the robust feature detection methods are compared for scale changes, rotation, blur, illumination changes and affine transformations. All the experiments use repeatability measurement and the number of correct matches for the evaluation measurements. SIFT presents its stability in most situations although its slow. SURF is the fastest one with good performance as the same as SIFT. PCA-SIFT show its advantages in rotation and illumination changes.},
author = {Juan, Luo and Gwun, O},
doi = {10.1007/s11270-006-2859-8},
file = {:Users/hanryham/Downloads/10.1.1.301.7041.pdf:pdf},
isbn = {1985-2304},
issn = {00496979},
journal = {International Journal of Image Processing (IJIP)},
keywords = {knn,pca sift,ransac,robust detectors,sift,surf},
mendeley-groups = {3D Reconstruction},
number = {4},
pages = {143--152},
title = {{A comparison of sift, pca-sift and surf}},
volume = {3},
year = {2009}
}
@article{Fahiem2007,
abstract = {3D reconstruction of objects form orthographic projections of engineering drawing has been very crucial research area since decades. In this paper, we have critically reviewed the latest approaches dealing with single view and multi view. Different approaches have been discussed with respect to bottom-up and topdown reconstruction. Many other parameters such as type of drawing, shape of object, x-sectional view, user interaction, dead end holes and hidden lines have also been compared amongst these approaches. {\textcopyright} 2007 IEEE.},
annote = {3D reconstruction of objects form orthographic of engineering drawing has been very crucial research area since decades. 3D 

Method:

Pros:

Conts:},
author = {Fahiem, Muhammad Abuzar and Haq, Shaiq A. and Saleemi, Farhat},
doi = {10.1109/GMAI.2007.9},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Fahiem, Haq, Saleemi - 2007 - A review of 3D reconstruction techniques from 2D orthographic line drawings.pdf:pdf},
isbn = {0769529011},
journal = {Geometric Modelling and Imaging, GMAI 2007, Proceedings},
mendeley-groups = {3D Reconstruction},
pages = {60--63},
title = {{A review of 3D reconstruction techniques from 2D orthographic line drawings}},
year = {2007}
}
@article{Hasinoff2007,
abstract = {This paper considers the problem of reconstructing visually realistic 3D models of dynamic semitransparent scenes, such as fire, from a very small set of simultaneous views (even two). We show that this problem is equivalent to a severely underconstrained computerized tomography problem, for which traditional methods break down. Our approach is based on the observation that every pair of photographs of a semitransparent scene defines a unique density field, called a Density Sheet, that 1 ) concentrates all its density on one connected, semitransparent surface, 2) reproduces the two photos exactly, and 3) is the most spatially compact density field that does so. From this observation, we reduce reconstruction to the convex combination of sheet-like density fields, each of which is derived from the Density Sheet of two input views. We have applied this method specifically to the problem of reconstructing 3D models of fire. Experimental results suggest that this method enables high-quality view synthesis without overfitting artifacts.},
author = {Hasinoff, Samuel W. and Kurulakos, Kiriakos N.},
doi = {10.1109/TPAMI.2007.1056},
file = {:Users/hanryham/Downloads/04135680.pdf:pdf},
isbn = {0162-8828},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {3D reconstruction,Computerized tomography,Fire,Image-based modeling,Image-based rendering,Semitransparent scenes,View synthesis,Volumetric reconstruction},
mendeley-groups = {3D Reconstruction},
number = {5},
pages = {870--885},
pmid = {17356205},
title = {{Photo-consistent reconstruction of semitransparent scenes by density-sheet decomposition}},
volume = {29},
year = {2007}
}
@article{Isenburg2006,
abstract = {We show how to greatly accelerate algorithms that compute Delaunay triangulations of huge, well-distributed point sets in 2D and 3D by exploiting the natural spatial coherence in a stream of points. We achieve large performance gains by introducing spatial finalization into point streams: we partition space into regions, and augment a stream of input points with finalization tags that indicate when a point is the last in its region. By extending an incremental algorithm for Delaunay triangulation to use finalization tags and produce streaming mesh output, we compute a billion-triangle terrain representation for the Neuse River system from 11.2 GB of LIDAR data in 48 minutes using only 70 MB of memory on a laptop with two hard drives. This is a factor of twelve faster than the previous fastest out-of-core Delaunay triangulation software.},
author = {Isenburg, Martin and Liu, Yuanxin and Shewchuk, Jonathan and Snoeyink, Jack},
doi = {10.1145/1141911.1141992},
file = {:Users/hanryham/Downloads/ilss-scdt-06.pdf:pdf},
isbn = {1595933646},
issn = {07300301},
journal = {ACM Transactions on Graphics},
keywords = {delaunay triangulation,geometry processing,processing,spatial finalization,stream,tin terrain model},
mendeley-groups = {3D Reconstruction},
number = {3},
pages = {1049},
title = {{Streaming computation of Delaunay triangulations}},
url = {http://portal.acm.org/citation.cfm?doid=1141911.1141992},
volume = {25},
year = {2006}
}
@article{RamosDiaz2005,
abstract = {In this paper, a novel method that permits to generate 3D video sequences using 2D real-life sequences is proposed. Reconstruction of 3D video sequence is realized using depth map computation and anaglyph synthesis. The depth map is formed employing the stereo matching technique based on global error energy minimization with smoothing functions. The anaglyph construction is implemented using the red component alignment interpolating the previously formed depth map. Additionally, the depth map transformation is realized in order to reduce the dynamic range of the disparity values, minimizing ghosting and enhancing color preservation. Several real-life color video sequences that contain different types of motions, such as translational, rotational, zoom and combination of previous ones are used demonstrating good visual performance of the proposed 3D video sequence reconstruction.},
annote = {geometric vs photometric},
author = {{Ramos Diaz}, E and Ponomaryov, V},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Ramos Diaz, Ponomaryov - 2005 - A Review of 3D Reconstruction from Video Sequences.pdf:pdf},
journal = {Revista Facultad de Ingenier Universidad de Antioquia},
mendeley-groups = {3D Reconstruction},
number = {56},
pages = {111--121},
title = {{A Review of 3D Reconstruction from Video Sequences}},
url = {http://cat.inist.fr/?aModele=afficheN{\&}cpsidt=23501536},
volume = {56},
year = {2005}
}
@article{Yao2003,
abstract = {Deformable 2D-3D medical image registration is an essential technique in Computer Integrated Surgery (CIS) to fuse 3D pre-operative data with 2D infra- operative data. Several factors may affect the accuracy of 2D-3D registration, including the number of 2D views, the angle between views, the view angle relative to anatomical objects, the co-registration error between views, the image noise, and the image distortion. In this paper, we investigate and assess the relationship between these factors and the accuracy of 2D-3D registration. We proposed a deformable 2D-3D registration method based on a statistical model. We conducted experiments using a hemi-pelvis model and simulated x-ray images. Some discussions are provided on how to improve the accuracy of 2D-3D registration based on our assessment.},
annote = {NULL},
author = {Yao, Jianhua and Taylor, Russell},
doi = {10.1109/ICCV.2003.1238644},
file = {:Users/hanryham/Library/Application Support/Mendeley Desktop/Downloaded/Yao, Taylor - 2003 - Assessing accuracy factors in deformable 2D3D medical image registration using a statistical pelvis model.pdf:pdf},
isbn = {0-7695-1950-4},
journal = {Proceedings of the IEEE International Conference on Computer Vision},
keywords = {Accuracy assessment,Deformable 2D-3D medical image registration,Statistical model},
mendeley-groups = {3D Reconstruction},
number = {Iccv},
pages = {1329--1334},
title = {{Assessing accuracy factors in deformable 2D/3D medical image registration using a statistical pelvis model}},
url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0344983014{\&}partnerID=tZOtx3y1},
volume = {2},
year = {2003}
}
@misc{Besl1992,
abstract = {This paper describes a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean square distance metric, and experience shows that the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of "shape complexity," one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. For example, a given "model" shape and a sensed "data" shape that represents a major portion of the model shape can be registered in minutes by testing one initial translation and a relatively small set of rotations to allow for the given level of model complexity. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model prior to shape inspection. The described method is also useful for deciding fundamental issues such as the congruence (shape equivalence) of different geometric representations as well as for estimating the motion between point sets where the correspondences are not known. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces.},
archivePrefix = {arXiv},
arxivId = {1706.05886},
author = {Besl, Paul and McKay, Neil},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
doi = {10.1109/34.121791},
eprint = {1706.05886},
file = {:Users/hanryham/Downloads/00121791.pdf:pdf},
isbn = {9781605588056},
issn = {0162-8828},
keywords = {3-D registration,free-form curve matching,free-form surface matching,motion estimation,pose estimation,quaternions},
mendeley-groups = {3D Reconstruction},
number = {2},
pages = {239--256},
pmid = {12015197},
title = {{A Method for Registration of 3-D Shapes}},
volume = {14},
year = {1992}
}
@article{Bouguet1981,
abstract = {Pyramid LK $\Upsilon$르},
archivePrefix = {arXiv},
arxivId = {3629719},
author = {Bouguet, Jean-yves and Tarasenko, Viacheslav and Lucas, Bruce D and Kanade, Takeo},
doi = {10.1109/HPDC.2004.1323531},
eprint = {3629719},
file = {:Users/hanryham/Downloads/10.1.1.185.585.pdf:pdf},
isbn = {0966-842X (Print)$\backslash$n0966-842X (Linking)},
issn = {0966842X},
journal = {Imaging},
keywords = {image pyramids,images,lucas kanade algorithm,opencv library,pixel coordinate vector is,then the lower right,tracking and motion},
mendeley-groups = {3D Reconstruction},
number = {x},
pages = {1--9},
pmid = {16140533},
title = {{Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm}},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1. 49.2019{\&}rep=rep1{\&}type=pdf{\%}5Cnhttp://trac.assembla.com /dilz{\_}mgr/export/272/doc/ktl-tracking/algo{\_}tracking.pdf},
volume = {130},
year = {1981}
}
