CLC number: TP391
On-line Access: 2021-05-17
Received: 2020-02-11
Revision Accepted: 2020-03-23
Crosschecked: 2020-12-29
Cited: 0
Clicked: 5763
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0002-1802-8197
https://orcid.org/0000-0001-7722-7172
https://orcid.org/0000-0002-3045-624X
https://orcid.org/0000-0002-8043-0312
Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin. Deep 3D reconstruction: methods, data, and challenges[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 652-672.
@article{title="Deep 3D reconstruction: methods, data, and challenges",
author="Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="5",
pages="652-672",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000068"
}
%0 Journal Article
%T Deep 3D reconstruction: methods, data, and challenges
%A Caixia Liu
%A Dehui Kong
%A Shaofan Wang
%A Zhiyong Wang
%A Jinghua Li
%A Baocai Yin
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 5
%P 652-672
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000068
TY - JOUR
T1 - Deep 3D reconstruction: methods, data, and challenges
A1 - Caixia Liu
A1 - Dehui Kong
A1 - Shaofan Wang
A1 - Zhiyong Wang
A1 - Jinghua Li
A1 - Baocai Yin
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 5
SP - 652
EP - 672
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000068
Abstract: Three-dimensional (3D) reconstruction of shapes is an important research topic in the fields of computer vision, computer graphics, pattern recognition, and virtual reality. Existing 3D reconstruction methods usually suffer from two bottlenecks: (1) they involve multiple manually designed states which can lead to cumulative errors, but can hardly learn semantic features of 3D shapes automatically; (2) they depend heavily on the content and quality of images, as well as precisely calibrated cameras. As a result, it is difficult to improve the reconstruction accuracy of those methods. 3D reconstruction methods based on deep learning overcome both of these bottlenecks by automatically learning semantic features of 3D shapes from low-quality images using deep networks. However, while these methods have various architectures, in-depth analysis and comparisons of them are unavailable so far. We present a comprehensive survey of 3D reconstruction methods based on deep learning. First, based on different deep learning model architectures, we divide 3D reconstruction methods based on deep learning into four types, recurrent neural network, deep autoencoder, generative adversarial network, and convolutional neural network based methods, and analyze the corresponding methodologies carefully. Second, we investigate four representative databases that are commonly used by the above methods in detail. Third, we give a comprehensive comparison of 3D reconstruction methods based on deep learning, which consists of the results of different methods with respect to the same database, the results of each method with respect to different databases, and the robustness of each method with respect to the number of views. Finally, we discuss future development of 3D reconstruction methods based on deep learning.
[1]Agarwal S, Snavely N, Simon I, et al., 2009. Building Rome in a day. IEEE 12th Int Conf on Computer Vision, p.72-79.
[2]Akhter I, Black MJ, 2015. Pose-conditioned joint angle limits for 3D human pose reconstruction. IEEE Conf on Computer Vision and Pattern Recognition, p.1446-1455.
[3]Bansal A, Russell B, Gupta A, 2016. Marr revisited: 2D-3D alignment via surface normal prediction. IEEE Conf on Computer Vision and Pattern Recognition, p.5965-5974.
[4]Bruna J, Zaremba W, Szlam A, et al., 2013. Spectral networks and locally connected networks on graphs. Int Conf on Learning Representations, p.1-14.
[5]Calakli F, Taubin G, 2011. SSD: smooth signed distance surface reconstruction. Comput Graph Forum, 30(7):1993-2002.
[6]Cao YP, Liu ZN, Kuang ZF, et al., 2018. Learning to reconstruct high-quality 3D shapes with cascaded fully convolutional networks. Proc 15th European Conf on Computer Vision, p.616-633.
[7]Chang AX, Funkhouser T, Guibas L, et al., 2015. ShapeNet: an information-rich 3D model repository. https://arxiv.org/abs/1512.03012
[8]Chen K, Lai YK, Hu SM, 2015. 3D indoor scene modeling from RGB-D data: a survey. Comput Vis Media, 1(4):267-278.
[9]Choy CB, Xu DF, Gwak J, et al., 2016. 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction. Proc 14th European Conf on Computer Vision, p.628-644.
[10]Cohen TS, Welling M, 2016. Group equivariant convolutional networks. Proc 33rd Int Conf on Machine Learning, p.2990-2999.
[11]Cohen TS, Geiger M, Köhler J, et al., 2018. Spherical CNNs. Int Conf on Learning Representations, p.1-15.
[12]Dai A, Qi CR, Niessner M, 2017. Shape completion using 3D-encoder-predictor CNNs and shape synthesis. IEEE Conf on Computer Vision and Pattern Recognition, p.6545-6554.
[13]Denton E, Chintala S, Szlam A, et al., 2015. Deep generative image models using a Laplacian pyramid of adversarial networks. Proc 28th Int Conf on Neural Information Processing Systems, p.1486-1494.
[14]Engel J, Schöps T, Cremers D, 2014. LSD-SLAM: large-scale direct monocular SLAM. Proc 13th European Conf on Computer Vision, p.834-849.
[15]Everingham M, Eslami SMA, van Gool L, et al., 2015. The PASCAL visual object classes challenge: a retrospective. Int J Comput Vis, 111(1):98-136.
[16]Fan HQ, Su H, Guibas L, 2017. A point set generation network for 3D object reconstruction from a single image. IEEE Conf on Computer Vision and Pattern Recognition, p.2463-2471.
[17]Fitzgibbon A, Zisserman A, 1998. Automatic 3D model acquisition and generation of new images from video sequences. Proc 9th European Signal Processing Conf, p.129-140.
[18]Furukawa Y, Ponce J, 2006. Carved visual hulls for image-based modeling. Proc 9th European Conf on Computer Vision, p.564-577.
[19]Gadelha M, Maji S, Wang R, 2017. 3D shape induction from 2D views of multiple objects. Int Conf on 3D Vision, p.402-411.
[20]Girdhar R, Fouhey DF, Rodriguez M, et al., 2016. Learning a predictable and generative vector representation for objects. Proc 14th European Conf on Computer Vision, p.484-499.
[21]Goesele M, Snavely N, Curless B, et al., 2007. Multi-view stereo for community photo collections. IEEE 11th Int Conf on Computer Vision, p.1-8.
[22]Goodfellow I, 2016. NIPS tutorial: generative adversarial networks. https://arxiv.org/abs/1701.00160
[23]Goodfellow IJ, Pouget-Abadie J, Mirza M, et al., 2014. Generative adversarial nets. Proc 27th Int Conf on Neural Information Processing Systems, p.2672-2680.
[24]Graham B, 2014. Spatially-sparse convolutional neural networks. https://arxiv.org/abs/1409.6070v1
[25]Graham B, 2015. Sparse 3D convolutional neural networks. Proc British Machine Vision Conf, p.150.1-150.9.
[26]Gregor K, Danihelka I, Graves A, et al., 2015. DRAW: a recurrent neural network for image generation. Proc 32nd Int Conf on Machine Learning, p.1462-1471.
[27]Gulrajani I, Ahmed F, Arjovsky M, et al., 2017. Improved training of Wasserstein GANs. Advances in Neural Information Processing Systems, p.5767-5777.
[28]Gwak J, Choy CB, Chandraker M, et al., 2017. Weakly supervised 3D reconstruction with adversarial constraint. Int Conf on 3D Vision, p.263-272.
[29]Han XF, Laga H, Bennamoun M, 2019. Image-based 3D object reconstruction: state-of-the-art and trends in the deep learning era. IEEE Trans Patt Anal Mach Intell, 43(5):1578-1604.
[30]Han XG, Li Z, Huang HB, et al., 2017. High-resolution shape completion using deep neural networks for global structure and local geometry inference. IEEE Int Conf on Computer Vision, p.85-93.
[31]Häne C, Tulsiani S, Malik J, 2017. Hierarchical surface prediction for 3D object reconstruction. Int Conf on 3D Vision, p.412-420.
[32]Henderson P, Ferrari V, 2019. Learning single-image 3D reconstruction by generative modelling of shape, pose and shading. Int J Comput Vis, 128:835-854.
[33]Hochreiter S, Schmidhuber J, 1997. Long short-term memory. Neur Comput, 9(8):1735-1780.
[34]Hu WZ, Zhu SC, 2015. Learning 3D object templates by quantizing geometry and appearance spaces. IEEE Trans Patt Anal Mach Intell, 37(6):1190-1205.
[35]Huang QX, Wang H, Koltun V, 2015. Single-view reconstruction via joint analysis of image and shape collections. ACM Trans Graph, 34(4):87.
[36]Kipf TN, Welling M, 2017. Semi-supervised classification with graph convolutional networks. Int Conf on Learning Representations, p.1-13.
[37]Kong C, Lin CH, Lucey S, 2017. Using locally corresponding CAD models for dense 3D reconstructions from a single image. IEEE Conf on Computer Vision and Pattern Recognition, p.5603-5611.
[38]Krizhevsky A, Sutskever I, Hinton GE, 2012. ImageNet classification with deep convolutional neural networks. Proc 25th Int Conf on Neural Information Processing Systems, p.1-9.
[39]Laga H, 2019. A survey on deep learning architectures for image-based depth reconstruction. https://arxiv.org/abs/1906.06113
[40]Lhuillier M, Quan L, 2005. A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Trans Patt Anal Mach Intell, 27(3):418-433.
[41]Li C, Wand M, 2016. Precomputed real-time texture synthesis with Markovian generative adversarial networks. Proc 14th European Conf on Computer Vision, p.702-716.
[42]Li YY, Dai A, Guibas L, et al., 2015. Database-assisted object retrieval for real-time 3D reconstruction. Comput Graph Forum, 34(2):435-446.
[43]Lim JJ, Pirsiavash H, Torralba A, 2014. Parsing IKEA objects: fine pose estimation. IEEE Int Conf on Computer Vision, p.2992-2999.
[44]Lin CH, Kong C, Lucey S, 2018. Learning efficient point cloud generation for dense 3D object reconstruction. AAAI Conf on Artificial Intelligence, p.7114-7121.
[45]Liu SC, Chen WK, Li TY, et al., 2019. Soft rasterizer: differentiable rendering for unsupervised single-view mesh reconstruction. https://arxiv.org/abs/1901.05567v1
[46]Lun ZL, Gadelha M, Kalogerakis E, et al., 2017. 3D shape reconstruction from sketches via multi-view convolutional networks. Int Conf on 3D Vision, p.67-77.
[47]Nan LL, Xie K, Sharf A, 2012. A search-classify approach for cluttered indoor scene understanding. ACM Trans Graph, 31(6):137.1-137.10.
[48]Nash C, Williams CKI, 2017. The shape variational autoencoder: a deep generative model of part-segmented 3D objects. Comput Graph Forum, 36(5):1-12.
[49]Newell A, Yang KY, Deng J, 2016. Stacked hourglass networks for human pose estimation. Proc 14th European Conf on Computer Vision, p.483-499.
[50]Niu CJ, Li J, Xu K, 2018. Im2Struct: recovering 3D shape structure from a single RGB image. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.1-9.
[51]Pontes JK, Kong C, Eriksson A, et al., 2017. Compact model representation for 3D reconstruction. Int Conf on 3D Vision, p.88-96.
[52]Pontes JK, Kong C, Sridharan S, et al., 2018. Image2Mesh: a learning framework for single image 3D reconstruction. Proc 14th Asian Conf on Computer Vision, p.365-381.
[53]Radford A, Metz L, Chintala S, 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. Int Conf on Learning Representations, p.1-16.
[54]Rezende DJ, Eslami SMA, Mohamed S, et al., 2016. Unsupervised learning of 3D structure from images. Proc 30th Conf on Neural Information Processing Systems, p.4997-5005.
[55]Shao TJ, Xu WW, Zhou K, et al., 2012. An interactive approach to semantic modeling of indoor scenes with an RGBD camera. ACM Trans Graph, 31(6):136.
[56]Shi YF, Long PX, Xu K, et al., 2016. Data-driven contextual modeling for 3D scene understanding. Comput Graph, 55:55-67.
[57]Silberman N, Hoiem D, Kohli P, et al., 2012. Indoor segmentation and support inference from RGBD images. Proc 12th European Conf on Computer Vision, p.746-760.
[58]Simonyan K, Zisserman A, 2015. Very deep convolutional networks for large-scale image recognitions. Int Conf on Learning Representations, p.1-14.
[59]Smith EJ, Meger D, 2017. Improved adversarial systems for 3D object generation and reconstruction. Proc 1st Annual Conf on Robot Learning, p.87-96.
[60]Sun XY, Wu JJ, Zhang XM, et al., 2018. Pix3D: dataset and methods for single-image 3D shape modeling. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.2974-2983.
[61]Sun YY, 2011. A survey of 3D reconstruction based on single image. J North China Univ Technol, 23(1):9-13 (in Chinese).
[62]Sundermeyer M, Schlüter R, Ney H, 2012. LSTM neural networks for language modeling. https://core.ac.uk/display/22066040
[63]Sutskever I, Vinyals O, Le Q, 2014. Sequence to sequence learning with neural networks. Proc 27th Int Conf on Neural Information Processing Systems, p.3104-3112.
[64]Tatarchenko M, Dosovitskiy A, Brox T, 2017. Octree generating networks: efficient convolutional architectures for high-resolution 3D outputs. IEEE Int Conf on Computer Vision, p.2107-2115.
[65]Udayan JD, Kim H, Kim JI, 2015. An image-based approach to the reconstruction of ancient architectures by extracting and arranging 3D spatial components. Front Inform Technol Electron Eng, 16(1):12-27.
[66]Varley J, DeChant C, Richardson A, et al., 2017. Shape completion enabled robotic grasping. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.2442-2447.
[67]Wang LJ, Fang Y, 2017. Unsupervised 3D reconstruction from a single image via adversarial learning. https://arxiv.org/abs/1711.09312
[68]Wang NY, Zhang YD, Li ZW, et al., 2018. Pixel2Mesh: generating 3D mesh models from single RGB images. Proc 15th European Conf on Computer Vision, p.55-71.
[69]Wang XL, Gupta A, 2016. Generative image modeling using style and structure adversarial networks. Proc 14th European Conf on Computer Vision, p.318-335.
[70]Wu JJ, Zhang CK, Xue TF, et al., 2016a. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. Advances in Neural Information Processing Systems, p.82-90.
[71]Wu JJ, Xue TF, Lim JJ, et al., 2016b. Single image 3D interpreter network. Proc 14th European Conf on Computer Vision, p.365-382.
[72]Wu JJ, Wang YF, Xue TF, et al., 2017. MarrNet: 3D shape reconstruction via 2.5D sketches. Advances in Neural Information Processing Systems, p.540-550.
[73]Wu ZR, Song SR, Khosla A, et al., 2015. 3D ShapeNets: a deep representation for volumetric shapes. IEEE Conf on Computer Vision and Pattern Recognition, p.1912-1920.
[74]Xiang Y, Mottaghi R, Savarese S, 2014. Beyond PASCAL: a benchmark for 3D object detection in the wild. IEEE Winter Conf on Applications of Computer Vision, p.75-82.
[75]Xiang Y, Kim W, Chen W, et al., 2016. ObjectNet3D: a large scale database for 3D object recognition. Proc 14th European Conf on Computer Vision, p.160-176.
[76]Xiao JX, Hays J, Ehinger KA, et al., 2010. SUN database: large-scale scene recognition from abbey to zoo. IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.3485-3492.
[77]Xie HZ, Yao HX, Sun XS, et al., 2019. Pix2Vox: context-aware 3D reconstruction from single and multi-view images. IEEE/CVF Int Conf on Computer Vision, p.1-9.
[78]Yan XC, Yang JM, Yumer E, et al., 2016. Perspective transformer nets: learning single-view 3D object reconstruction without 3D supervision. Advances in Neural Information Processing Systems, p.1696-1704.
[79]Yang B, Wen HK, Wang S, et al., 2018. 3D object reconstruction from a single depth view with adversarial learning. IEEE Int Conf on Computer Vision Workshop, p.679-688.
[80]Yang B, Rosa S, Markham A, et al., 2019. 3D object dense reconstruction from a single depth view. IEEE Trans Patt Anal Mach Intell, 41(12):2820-2834.
[81]Yang B, Wang S, Markham A, et al., 2020. Robust attentional aggregation of deep feature sets for multi-view 3D reconstruction. Int J Comput Vis, 128:53-73.
[82]Zeiler MD, Krishnan D, Taylor GW, et al., 2010. Deconvolutional networks. IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.2528-2535.
[83]Zhu CY, Byrd RH, Lu PH, et al., 1997. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans Math Softw, 23(4):550-560.
[84]Zou CH, Yumer E, Yang JM, et al., 2017. 3D-PRNN: generating shape primitives with recurrent neural networks. IEEE Int Conf on Computer Vision, p.900-909.
Open peer comments: Debate/Discuss/Question/Opinion
<1>