“Dual octree graph networks for learning adaptive volumetric shape representations” by Wang, Liu and Tong
Conference:
Type:
Title:
- Dual octree graph networks for learning adaptive volumetric shape representations
Presenter(s)/Author(s):
Abstract:
We present an adaptive deep representation of volumetric fields of 3D shapes and an efficient approach to learn this deep representation for high-quality 3D shape reconstruction and auto-encoding. Our method encodes the volumetric field of a 3D shape with an adaptive feature volume organized by an octree and applies a compact multilayer perceptron network for mapping the features to the field value at each 3D position. An encoder-decoder network is designed to learn the adaptive feature volume based on the graph convolutions over the dual graph of octree nodes. The core of our network is a new graph convolution operator defined over a regular grid of features fused from irregular neighboring octree nodes at different levels, which not only reduces the computational and memory cost of the convolutions over irregular neighboring octree nodes, but also improves the performance of feature learning. Our method effectively encodes shape details, enables fast 3D shape reconstruction, and exhibits good generality for modeling 3D shapes out of training categories. We evaluate our method on a set of reconstruction tasks of 3D shapes and scenes and validate its superiority over other existing approaches. Our code, data, and trained models are available at https://wang-ps.github.io/dualocnn.
References:
1. Matan Atzmon and Yaron Lipman. 2020. SAL: Sign agnostic learning of shapes from raw data. In CVPR.Google Scholar
2. Matan Atzmon and Yaron Lipman. 2021. SALD: Sign Agnostic Learning with Derivatives. In ICLR.Google Scholar
3. Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018).Google Scholar
4. Matthew Berger, Andrea Tagliasacchi, Lee M. Seversky, Pierre Alliez, Gaël Guennebaud, Joshua A. Levine, Andrei Sharf, and Claudio T. Silva. 2017. A survey of surface reconstruction from point clouds. Comput. Graph. Forum 36, 1 (2017).Google Scholar
5. Federica Bogo, Javier Romero, Gerard Pons-Moll, and Michael J Black. 2017. Dynamic FAUST: Registering human bodies in motion. In CVPR.Google Scholar
6. Robert Bridson. 2015. Fluid simulation for computer graphics. CRC press.Google ScholarDigital Library
7. Andrew Brock, Theodore Lim, J.M. Ritchie, and Nick Weston. 2016. Generative and discriminative voxel modeling with convolutional neural networks. In 3D deep learning workshop (NeurIPS).Google Scholar
8. Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. 2020. Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction. In ECCV.Google Scholar
9. Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. 2015. ShapeNet: An information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015).Google Scholar
10. Zhiqin Chen and Hao Zhang. 2019. Learning implicit fields for generative shape modeling. In CVPR.Google Scholar
11. Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. 2020. Implicit functions in feature space for 3d shape reconstruction and completion. In CVPR.Google Scholar
12. Christopher Choy, JunYoung Gwak, and Silvio Savarese. 2019. 4D spatio-temporal convnets: Minkowski convolutional neural networks. In CVPR.Google Scholar
13. Christopher Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese. 2016. 3D-R2N2: A unified approach for single and multi-view 3D object reconstruction. In ECCV.Google Scholar
14. Brian Curless and Marc Levoy. 1996. A Volumetric Method for Building Complex Models from Range Images. In SIGGRAPH.Google Scholar
15. Angela Dai, Charles R. Qi, and Matthias Niessner. 2017. Shape completion using 3D-encoder-predictor CNNs and shape synthesis. In CVPR.Google Scholar
16. Matthias Fey and Jan Eric Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop.Google Scholar
17. Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Müller. 2018. SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels. In CVPR.Google Scholar
18. Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser. 2020. Local Deep Implicit Functions for 3D Shape. In CVPR.Google Scholar
19. Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In ICML.Google Scholar
20. Benjamin Graham, Martin Engelcke, and Laurens van der Maaten. 2018. 3D semantic segmentation with submanifold sparse convolutional networks. In CVPR.Google Scholar
21. Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman. 2020. Implicit geometric regularization for learning shapes. In ICML.Google Scholar
22. Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry. 2018. AtlasNet: A Papier-Mâché approach to learning 3D surface generation. In CVPR.Google Scholar
23. Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R Martin, and Shi-Min Hu. 2021. PCT: Point cloud transformer. Computational Visual Media 7, 2 (2021).Google Scholar
24. Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan, Simon Stent, and Roberto Cipolla. 2016. SceneNet: Understanding Real World Indoor Scenes With Synthetic Data. In CVPR.Google Scholar
25. Christian Häne, Shubham Tulsiani, and Jitendra Malik. 2017. Hierarchical surface prediction for 3D object reconstruction. In 3DV.Google Scholar
26. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR.Google Scholar
27. Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, and Thomas Funkhouser. 2020. Local implicit grid representations for 3D scenes. In CVPR.Google Scholar
28. Tao Ju, Frank Losasso, Scott Schaefer, and Joe Warren. 2002. Dual contouring of hermite data. ACM Trans. Graph. (SIGGRAPH) (2002).Google Scholar
29. Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. 2006. Poisson surface reconstruction. In Symp. Geom. Proc.Google ScholarDigital Library
30. Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. In ICLR.Google Scholar
31. Alejandro León, Juan Carlos Torres, and Francisco Velasco. 2008. Volume octree with an implicitly defined dual grid. Computers & Graphics 32, 4 (2008).Google Scholar
32. Thomas Lewiner, Vinícius Mello, Adelailson Peixoto, Sinésio Pesco, and Hélio Lopes. 2010. Fast Generation of Pointerless Octree Duals. Computer Graphics Forum 29, 5 (2010).Google Scholar
33. Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. 2018. PointCNN: Convolution on X-transformed points. In NeurIPS.Google Scholar
34. Shi-Lin Liu, Hao-Xiang Guo, Hao Pan, Peng-Shuai Wang, Xin Tong, and Yang Liu. 2021. Deep Implicit Moving Least-Squares Functions for 3D Reconstruction. In CVPR.Google Scholar
35. William E. Lorensen and Harvey E. Cline. 1987. Marching Cubes: A High Resolution 3D Surface Construction Algorithm. In SIGGRAPH.Google Scholar
36. Julien N. P. Martel, David B. Lindell, Connor Z. Lin, Eric R. Chan, Marco Monteiro, and Gordon Wetzstein. 2021. ACORN: Adaptive coordinate networks for neural scene representation. ACM Trans. Graph. (SIGGRAPH) 40, 4 (2021).Google ScholarDigital Library
37. Daniel Maturana and Sebastian Scherer. 2015. VoxNet: A 3D convolutional neural network for real-time object recognition. In IROS.Google Scholar
38. Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. 2019. Occupancy networks: Learning 3D reconstruction in function space. In CVPR.Google Scholar
39. Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. 2020. NeRF: Representing scenes as neural radiance fields for view synthesis. In ECCV.Google Scholar
40. Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. 2022. Instant Neural Graphics Primitives with a Multiresolution Hash Encoding. arXiv preprint arXiv:2201.05989 (2022).Google Scholar
41. Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, and Andreas Geiger. 2019. Texture fields: Learning texture representations in function space. In ICCV. Yutaka Ohtake, Alexander Belyaev, Marc Alexa, Greg Turk, and Hans-Peter Seidel. 2003. Multi-level partition of unity implicits. ACM Trans. Graph. (SIGGRAPH) 22, 3 (2003).Google Scholar
42. A Cengiz Öztireli, Gael Guennebaud, and Markus Gross. 2009. Feature preserving point set surfaces based on non-linear kernel regression. Comput. Graph. Forum (EG) 28, 2 (2009).Google Scholar
43. Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. 2019. DeepSDF: Learning continuous signed distance functions for shape representation. In CVPR.Google Scholar
44. Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. 2020. Convolutional occupancy networks. In ECCV.Google Scholar
45. Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. 2017a. PointNet: Deep learning on point sets for 3D classification and segmentation. In CVPR.Google Scholar
46. Charles R. Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J.Google Scholar
47. Guibas. 2016. Volumetric and multi-view CNNs for object classification on 3D data. In CVPR.Google Scholar
48. Charles R. Qi, Li Yi, Hao Su, and Leonidas J. Guibas. 2017b. PointNet++: Deep hierarchical feature learning on point sets in a metric space. In NeurIPS.Google Scholar
49. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention.Google ScholarCross Ref
50. Scott Schaefer and Joe Warren. 2004. Dual marching cubes: Primal contouring of dual grids. In Pacific Graphics.Google Scholar
51. Tianjia Shao, Yin Yang, Yanlin Weng, Qiming Hou, and Kun Zhou. 2018. H-CNN: spatial hashing based CNN for 3D shape analysis. IEEE. T. Vis. Comput. Gr. (2018).Google Scholar
52. Andrei Sharf, Thomas Lewiner, Gil Shklarski, Sivan Toledo, and Daniel Cohen-Or. 2007. Interactive topology-aware surface reconstruction. ACM Trans. Graph. (SIGGRAPH) 26, 3 (2007).Google ScholarDigital Library
53. Martin Simonovsky and Nikos Komodakis. 2017. Dynamic edge-conditioned filters in convolutional neural networks on graphs. In CVPR.Google Scholar
54. Vincent Sitzmann, Julien NP Martel, Alexander W Bergman, David B Lindell, and Gordon Wetzstein. 2020. Implicit Neural Representations with Periodic Activation Functions. In NeurIPS.Google Scholar
55. Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. 2021. Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes. In CVPR.Google Scholar
56. Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng. 2020. Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains. In NeurIPS.Google Scholar
57. Jia-Heng Tang, Weikai Chen, Jie Yang, Bo Wang, Songrun Liu, Bo Yang, and Lin Gao. 2021. OctField: Hierarchical Implicit Functions for 3D Modeling. In NeurIPS.Google Scholar
58. Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox. 2017. Octree generating networks: efficient convolutional architectures for high-resolution 3D outputs. In ICCV.Google Scholar
59. Hugues Thomas, Charles R. Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J. Guibas. 2019. KPConv: Flexible and deformable convolution for point clouds. In ICCV.Google Scholar
60. Benjamin Ummenhofer and Vladlen Koltun. 2021. Adaptive Surface Reconstruction With Multiscale Convolutional Kernels. In ICCV.Google Scholar
61. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR.Google Scholar
62. Hao Wang, Nadav Schor, Ruizhen Hu, Haibin Huang, Daniel Cohen-Or, and Hui Huang. 2018a. Global-to-local generative model for 3D shapes. ACM Trans. Graph. (SIGGRAPH ASIA) 37, 6 (2018).Google Scholar
63. Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong. 2017. O-CNN: Octree-based convolutional neural networks for 3D shape analysis. ACM Trans. Graph. (SIGGRAPH) 36, 4 (2017).Google ScholarDigital Library
64. Peng-Shuai Wang, Yang Liu, and Xin Tong. 2020. Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion. In CVPR Workshop.Google ScholarCross Ref
65. Peng-Shuai Wang, Chun-Yu Sun, Yang Liu, and Xin Tong. 2018b. Adaptive O-CNN: A patch-based deep representation of 3D shapes. ACM Trans. Graph. (SIGGRAPH ASIA) 37, 6 (2018).Google Scholar
66. Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. 2019. Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. 38, 5 (2019).Google ScholarDigital Library
67. Francis Williams, Zan Gojcic, Sameh Khamis, Denis Zorin, Joan Bruna, Sanja Fidler, and Or Litany. 2022. Neural Fields as Learnable Kernels for 3D Reconstruction. In CVPR.Google Scholar
68. Francis Williams, Matthew Trager, Joan Bruna, and Denis Zorin. 2021. Neural splines: Fitting 3D surfaces with infinitely-wide neural networks. In CVPR.Google Scholar
69. Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum. 2016. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In NeurIPS.Google ScholarDigital Library
70. Wenxuan Wu, Zhongang Qi, and Li Fuxin. 2019. PointConv: Deep Convolutional Networks on 3D Point Clouds. In CVPR.Google Scholar
71. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32, 1 (2020).Google Scholar
72. Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 2015. 3D ShapeNets: A deep representation for volumetric shape modeling. In CVPR.Google Scholar
73. Hongyi Xu and Jernej Barbič. 2014. Signed Distance Fields for Polygon Soup Meshes. In Proceedings of Graphics Interface.Google Scholar
74. Mutian Xu, Runyu Ding, Hengshuang Zhao, and Xiaojuan Qi. 2021. PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds. In CVPR.Google Scholar
75. Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao. 2018. SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters. In ECCV.Google Scholar
76. Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip Torr, and Vladlen Koltun. 2021. Point transformer. In ICCV.Google Scholar
77. Kun Zhou, Minmin Gong, Xin Huang, and Baining Guo. 2011. Data-parallel octrees for surface reconstruction. IEEE. T. Vis. Comput. Gr. 17, 5 (2011).Google Scholar
78. Received January 2022; accepted March 2022; final version May 2022Google Scholar