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2016 | OriginalPaper | Buchkapitel

3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction

verfasst von : Christopher B. Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, Silvio Savarese

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2). The network learns a mapping from images of objects to their underlying 3D shapes from a large collection of synthetic data [13]. Our network takes in one or more images of an object instance from arbitrary viewpoints and outputs a reconstruction of the object in the form of a 3D occupancy grid. Unlike most of the previous works, our network does not require any image annotations or object class labels for training or testing. Our extensive experimental analysis shows that our reconstruction framework (i) outperforms the state-of-the-art methods for single view reconstruction, and (ii) enables the 3D reconstruction of objects in situations when traditional SFM/SLAM methods fail (because of lack of texture and/or wide baseline).

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Literatur
1.
2.
3.
Dosovitskiy, A., Springenberg, J.T., Brox, T.: Learning to generate chairs with convolutional neural networks. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
4.
Agarwal, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building rome in a day. In: 2009 IEEE 12th International Conference on Computer Vision. IEEE (2009)
5.
Anwar, Z., Ferrie, F.: Towards robust voxel-coloring: handling camera calibration errors and partial emptiness of surface voxels. In: Proceedings of the 18th International Conference on Pattern Recognition, ICPR 2006, vol. 1. IEEE Computer Society, Washington, DC, USA (2006). doi:10.​1109/​ICPR.​2006.​1129
6.
Bao, Y., Chandraker, M., Lin, Y., Savarese, S.: Dense object reconstruction using semantic priors. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (2013)
7.
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)CrossRef
8.
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: a CPU and GPU math expression compiler. In: Proceedings of the Python for Scientific Computing Conference (SciPy), June 2010
9.
Bhat, D.N., Nayar, S.K.: Ordinal measures for image correspondence. IEEE Trans. Pattern Anal. Mach. Intell. 20(4), 415–423 (1998)CrossRef
10.
Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1063–1074 (2003)CrossRef
11.
Bongsoo Choy, C., Stark, M., Corbett-Davies, S., Savarese, S.: Enriching object detection with 2D–3D registration and continuous viewpoint estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015
12.
Broadhurst, A., Drummond, T.W., Cipolla, R.: A probabilistic framework for space carving. In: Eighth IEEE International Conference on Computer Vision, ICCV 2001, Proceedings, vol. 1. IEEE (2001)
13.
Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F.: ShapeNet: an information-rich 3D model repository. Technical report, Stanford University, Princeton University, Toyota Technological Institute at Chicago (2015)
14.
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. ArXiv e-prints arXiv:​1406.​1078 (2014)
15.
16.
Dame, A., Prisacariu, V.A., Ren, C.Y., Reid, I.: Dense reconstruction using 3D object shape priors. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)
17.
Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. Adv. Neural Inf. Process. Syst. 27, 11:1–11:15 (2014)
18.
Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part II. LNCS, vol. 8690, pp. 834–849. Springer, Heidelberg (2014)
19.
Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes challenge 2012 (2011)
20.
Firman, M., Mac Aodha, O., Julier, S., Brostow, G.J.: Structured prediction of unobserved voxels from a single depth image. In: CVPR (2016)
21.
Fitzgibbon, A., Zisserman, A.: Automatic 3D model acquisition and generation of new images from video sequences. In: 9th European Signal Processing Conference (EUSIPCO 1998). IEEE (1998)
22.
Fuentes-Pacheco, J., Ruiz-Ascencio, J., Rendón-Mancha, J.M.: Visual simultaneous localization and mapping: a survey. Artif. Intell. Rev. 43(1), 55–81 (2015)CrossRef
23.
Slabaugh, G.G., Culbertson, W.B., Malzbender, T., Stevens, M.R., Schafer, R.W.: Methods for volumetric reconstruction of visual scenes. Int. J. Comput. Vis. 57(3), 179–199 (2004)CrossRef
24.
Häming, K., Peters, G.: The structure-from-motion reconstruction pipeline-a survey with focus on short image sequences. Kybernetika 46(5), 926–937 (2010)MATHMathSciNet
25.
Häne, C., Savinov, N., Pollefeys, M.: Class specific 3D object shape priors using surface normals. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
26.
27.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
28.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
29.
Hoiem, D., Efros, A.A., Hebert, M.: Automatic photo pop-up. ACM Trans. Graph. (TOG) 24(3), 577–584 (2005)CrossRef
30.
Kar, A., Tulsiani, S., Carreira, J., Malik, J.: Category-specific object reconstruction from a single image. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2015)
31.
Kemelmacher-Shlizerman, I., Basri, R.: 3D face reconstruction from a single image using a single reference face shape. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 394–405 (2011)CrossRef
32.
33.
Kutulakos, K.N., Seitz, S.M.: A theory of shape by space carving. Int. J. Comput. Vis. 38(3), 199–218 (2000)CrossRefMATH
34.
Lawrence, G.R.: Machine perception of three-dimensional solids. Ph.D. thesis (1963)
35.
Lhuillier, M., Quan, L.: A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 418–433 (2005)CrossRef
36.
Liu, F., Shen, C., Lin, G.: Deep convolutional neural fields for depth estimation from a single image. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition (2015)
37.
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comp. Vis. 60(2), 145–166 (2004)CrossRef
38.
Matthews, I., Xiao, J., Baker, S.: 2D vs. 3D deformable face models: representational power, construction, and real-time fitting. Int. J. Comput. Vis. 75(1), 93–113 (2007)CrossRef
39.
Nevatia, R., Binford, T.O.: Description and recognition of curved objects. Artif. Intell. 8(1), 77–98 (1977)CrossRefMATH
40.
Prisacariu, V.A., Segal, A.V., Reid, I.: Simultaneous monocular 2D segmentation, 3D pose recovery and 3D reconstruction. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 593–606. Springer, Heidelberg (2013)CrossRef
41.
Rock, J., Gupta, T., Thorsen, J., Gwak, J., Shin, D., Hoiem, D.: Completing 3D object shape from one depth image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
42.
Sandhu, R., Dambreville, S., Yezzi, A., Tannenbaum, A.: A nonrigid kernel-based framework for 2D–3D pose estimation and 2D image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(6), 1098–1115 (2011)CrossRef
43.
Saponaro, P., Sorensen, S., Rhein, S., Mahoney, A.R., Kambhamettu, C.: Reconstruction of textureless regions using structure from motion and image-based interpolation. In: 2014 IEEE International Conference on Image Processing (ICIP). IEEE (2014)
44.
Saxena, A., Sun, M., Ng, A.Y.: Make3D: learning 3D scene structure from a single still image. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 824–840 (2009)CrossRef
45.
Seitz, S.M., Dyer, C.R.: Photorealistic scene reconstruction by voxel coloring. Int. J. Comput. Vis. 35(2), 151–173 (1999)CrossRef
46.
Song, H.O., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. ArXiv e-prints arXiv:​1511.​06452 (2015)
47.
48.
Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: INTERSPEECH (2012)
49.
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (2014)
50.
Vicente, S., Carreira, J., Agapito, L., Batista, J.: Reconstructing PASCAL VOC. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
51.
Xiang, Y., Mottaghi, R., Savarese, S.: Beyond pascal: A benchmark for 3D object detection in the wild. In: 2014 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE (2014)
52.
Zia, M.Z., Stark, M., Schiele, B., Schindler, K.: Detailed 3D representations for object modeling and recognition. In: TPAMI (2013)
Metadaten
Titel
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
verfasst von
Christopher B. Choy
Danfei Xu
JunYoung Gwak
Kevin Chen
Silvio Savarese
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46483-1

Electronic ISBN: 978-3-319-46484-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46484-8_38

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