research-article

Open access

DeltaConv: anisotropic operators for geometric deep learning on point clouds

Authors:

Elmar Eisemann,

Klaus HildebrandtAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 41, Issue 4

Article No.: 105, Pages 1 - 10

https://doi.org/10.1145/3528223.3530166

Published: 22 July 2022 Publication History

Abstract

Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep learning on images and the increased availability of 3D data. In this paper, we aim to construct anisotropic convolution layers that work directly on the surface derived from a point cloud. This is challenging because of the lack of a global coordinate system for tangential directions on surfaces. We introduce DeltaConv, a convolution layer that combines geometric operators from vector calculus to enable the construction of anisotropic filters on point clouds. Because these operators are defined on scalar- and vector-fields, we separate the network into a scalar- and a vector-stream, which are connected by the operators. The vector stream enables the network to explicitly represent, evaluate, and process directional information. Our convolutions are robust and simple to implement and match or improve on state-of-the-art approaches on several benchmarks, while also speeding up training and inference.

Supplemental Material

MP4 File

presentation

Download
312.68 MB

SRT File

presentation

Download
24.61 KB

ZIP File

supplemental material

Download
2.71 MB

ZIP File

Code for the paper "DeltaConv: anisotropic operators for geometric deep learning on point clouds" presented in SIGGRAPH 2022 and published in ACM Transactions on Graphics (TOG). The code is also available via GitHub: http://www.replicabilitystamp.org#https-github-com-rubenwiersma-deltaconv

Download
34.16 MB

References

[1]

Matan Atzmon, Haggai Maron, Yaron Lipman, Atzmon Matan, Maron Haggai, and Lipman Yaron. 2018. Point convolutional neural networks by extension operators. ACM Trans. Graph. 37, 4 (2018), 71:1--71:12.

Digital Library

[2]

Yizhak Ben-Shabat, Michael Lindenbaum, and Anath Fischer. 2018. 3DmFV: Three-Dimensional Point Cloud Classification in Real-Time Using Convolutional Neural Networks. IEEE Robotics and Automation Letters 3 (2018), 3145--3152.

[3]

Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael Bronstein. 2016. Learning shape correspondence with anisotropic convolutional neural networks. NeurIPS (2016).

[4]

Alexandre Boulch. 2020. ConvPoint: Continuous convolutions for point cloud processing. Comput. Graph. Forum 88 (2020), 24--34.

[5]

Christopher Brandt, Leonardo Scandolo, Elmar Eisemann, and Klaus Hildebrandt. 2017. Spectral Processing of Tangential Vector Fields. Computer Graphics Forum 36, 6 (2017), 338--353.

Digital Library

[6]

Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković. 2021. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv preprint arXiv:2104.13478 (2021).

[7]

Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017. Geometric Deep Learning: Going beyond Euclidean data. IEEE Signal Process. Mag. 34 (2017), 18--42.

[8]

Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral Networks and Locally Connected Networks on Graphs. ICLR (2014).

[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. Technical Report arXiv:1512.03012.

[10]

Jintai Chen, Biwen Lei, Qingyu Song, Haochao Ying, Danny Z Chen, and Jian Wu. 2020. A Hierarchical Graph Network for 3D Object Detection on Point Clouds. CVPR (2020).

[11]

Christopher Choy, JunYoung Gwak, and Silvio Savarese. 2019. 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR (2019).

[12]

Taco Cohen, Mario Geiger, Jonas Koehler, and Max Welling. 2018. Spherical CNNs. ICLR (2018).

[13]

Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, and Max Welling. 2019. Gauge Equivariant Convolutional Networks and the Icosahedral CNN. ICML (2019).

[14]

Keenan Crane, Fernando de Goes, Mathieu Desbrun, and Peter Schroder. 2013a. Digital Geometry Processing with Discrete Exterior Calculus. SIGGRAPH Asia 2013 Courses (2013), 7:1--7:126.

[15]

Keenan Crane, Clarisse Weischedel, and Max Wardetzky. 2013b. Geodesics in heat: A new approach to computing distance based on heat flow. ACM Trans. Graph. 32, 5 (2013), 152:1--152:11.

Digital Library

[16]

Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. 2017. ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. CVPR (2017).

[17]

Fernando de Goes, Mathieu Desbrun, and Yiying Tong. 2016. Vector Field Processing on Triangle Meshes. SIGGRAPH Asia 2016 Courses (2016), 27:1--27:49.

[18]

Pim de Haan, Maurice Weiler, Taco Cohen, and Max Welling. 2021. Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. ICLR (2021).

[19]

C. Deng, O. Litany, Y. Duan, A. Poulenard, A. Tagliasacchi, and L. Guibas. 2021. Vector Neurons: A General Framework for SO(3)-Equivariant Networks. In ICCV.

[20]

Miguel Dominguez, Rohan Dhamdhere, Atir Petkar, Saloni Jain, Shagan Sah, and Raymond Ptucha. 2018. General-Purpose Deep Point Cloud Feature Extractor. WACV (2018).

[21]

Moshe Eliasof and Eran Treister. 2020. DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling. NeurIPS (2020).

[22]

Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, and Kostas Daniilidis. 2017. Learning SO(3) Equivariant Representations with Spherical CNNs. ECCV (2017).

[23]

Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. 2019. Hypergraph neural networks. AAAI (2019).

[24]

Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. ICLR Workshop on Representation Learning on Graphs and Manifolds (2019).

[25]

Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Müller. 2018. SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels. CVPR (2018).

[26]

Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling. 2020. SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. NeurIPS (2020).

[27]

Jan E Gerken, Jimmy Aronsson, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, and Daniel Persson. 2021. Geometric deep learning and equivariant neural networks. arXiv preprint arXiv:2105.13926 (2021).

[28]

Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, and Jia Deng. 2021. Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline. ICML (2021).

[29]

Benjamin Graham, Martin Engelcke, and Laurens van der Maaten. 2018. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks. CVPR (2018).

[30]

Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. 2020. Deep Learning for 3D Point Clouds: A Survey. IEEE TPAMI 43 (2020), 4338--4364.

Digital Library

[31]

Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, and Daniel Cohen-Or. 2019. MeshCNN: A Network with an Edge. ACM Trans. Graph. 38, 4 (2019), 90:1--90:12.

Digital Library

[32]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. CVPR (2016).

[33]

Pedro Hermosilla, Tobias Ritschel, Pere-Pau Vázquez, Àlvar Vinacua, and Timo Ropinski. 2018. Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds. ACM Trans. Graph. 37, 6 (2018), 235:1--235:12.

Digital Library

[34]

Binh-Son Hua, Quang-Hieu Pham, Duc Thanh Nguyen, Minh-Khoi Tran, Lap-Fai Yu, and Sai-Kit Yeung. 2016. SceneNN: A Scene Meshes Dataset with aNNotations. 3DV (2016).

[35]

Binh-Son Hua, Minh-Khoi Tran, and Sai-Kit Yeung. 2018. Pointwise Convolutional Neural Networks. CVPR (2018).

[36]

Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. ICML (2015).

Digital Library

[37]

Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, and Matthias Nießner. 2019. Spherical CNNs on Unstructured Grids. ICLR (2019).

[38]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR.

[39]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. ICLR (2017).

[40]

Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, and Joan Bruna. 2018. Surface Networks. CVPR (2018).

[41]

Alon Lahav and A. Tal. 2020. MeshWalker: deep mesh understanding by random walks. ACM Trans. Graph. 39, 6 (2020), 263:1--263:13.

Digital Library

[42]

Shiyi Lan, Ruichi Yu, Gang Yu, and L Davis. 2019. Modeling Local Geometric Structure of 3D Point Clouds Using Geo-CNN. CVPR (2019).

[43]

Eric-Tuan Le, Iasonas Kokkinos, and Niloy J Mitra. 2020. Going Deeper With Lean Point Networks. CVPR (2020).

[44]

Huan Lei, Naveed Akhtar, and Ajmal Mian. 2019. Octree guided CNN with Spherical Kernels for 3D Point Clouds. CVPR (2019).

[45]

Yangyan Li, Bu Rui, Mingchao Sun, Wei Wu, Xinhan Di, Baoquan Chen, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. 2018. PointCNN: Convolution on x-transformed points. NeurIPS (2018).

[46]

Zhouhui et al. Lian. 2011. SHREC '11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes. Eurographics Workshop on 3D Object Retrieval (01 2011), 79--88.

[47]

Jian Liang and Hongkai Zhao. 2013. Solving Partial Differential Equations on Point Clouds. SIAM J. Sci. Comput. 35 (2013), A1461--A1486.

Digital Library

[48]

Liqiang Lin, Pengdi Huang, Chi-Wing Fu, Kai Xu, Hao Zhang, and Hui Huang. 2020. One Point is All You Need: Directional Attention Point for Feature Learning. arXiv preprint arXiv:2012.06257 (2020).

[49]

Hsueh-Ti Derek Liu, Alec Jacobson, and Keenan Crane. 2017. A Dirac Operator for Extrinsic Shape Analysis. Comput. Graph. Forum 36, 5 (2017), 139--149.

Digital Library

[50]

Jinxian Liu, Bingbing Ni, Caiyuan Li, Jiancheng Yang, and Qi Tian. 2019c. Dynamic Points Agglomeration for Hierarchical Point Sets Learning. ICCV (2019).

[51]

Weiping Liu, Jia Sun, Wanyi Li, Ting Hu, and Peng Wang. 2019d. Deep Learning on Point Clouds and Its Application: A Survey. Sens 19 (2019), 4188.

[52]

Yongcheng Liu, Bin Fan, Gaofeng Meng, Jiwen Lu, Shiming Xiang, and Chunhong Pan. 2019a. DensePoint: Learning densely contextual representation for efficient point cloud processing. ICCV (2019).

[53]

Yongcheng Liu, Bin Fan, Shiming Xiang, and Chunhong Pan. 2019b. Relation-Shape Convolutional Neural Network for Point Cloud Analysis. CVPR (2019).

[54]

Ze Liu, Han Hu, Yue Cao, Zheng Zhang, and Xin Tong. 2020. A Closer Look at Local Aggregation Operators in Point Cloud Analysis. ECCV (2020).

[55]

Ilya Loshchilov and Frank Hutter. 2017. SGDR: Stochastic Gradient Descent with Warm Restarts. ICLR (2017).

[56]

Tao Lu, Limin Wang, and Gangshan Wu. 2021. CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation. CVPR (2021).

[57]

Francesco Milano, Antonio Loquercio, Antoni Rosinol, Davide Scaramuzza, and Luca Carlone. 2020. Primal-Dual Mesh Convolutional Neural Networks. NeurIPS (2020).

[58]

Thomas W. Mitchel, Vladimir G. Kim, and Michael Kazhdan. 2021. Field Convolutions for Surface CNNs. ICCV (2021).

[59]

Andrew Nealen. 2004. An As-Short-as-possible introduction to the least squares, weighted least squares and moving least squares methods for scattered data approximation and interpolation. URL: http://www.nealen.com/projects/mls/asapmls.pdf 1 (2004).

[60]

B. O'Neill. 1983. Semi-Riemannian Geometry With Applications to Relativity. Elsevier Science.

[61]

Guanghua Pan, Jun Wang, Rendong Ying, and Peilin Liu. 2018. 3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN. arXiv preprint arXiv:1812.06254 (2018).

[62]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. NeurIPS (2019).

[63]

P Perona and J Malik. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE TPAMI 12, 7 (1990), 629--639.

Digital Library

[64]

Adrien Poulenard and Maks Ovsjanikov. 2018. Multi-directional geodesic neural networks via equivariant convolution. ACM Trans. Graph. 37, 6 (2018), 236:1--236:14.

Digital Library

[65]

Adrien Poulenard, Marie-Julie Rakotosaona, Yann Ponty, and Maks Ovsjanikov. 2019. Effective Rotation-invariant Point CNN with Spherical Harmonics kernels. 3DV (2019).

[66]

Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017a. PointNet: Deep learning on point sets for 3D classification and segmentation. CVPR (2017).

[67]

Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. 2017b. PointNet++: Deep hierarchical feature learning on point sets in a metric space. NeurIPS (2017).

[68]

Shi Qiu, Saeed Anwar, and Nick Barnes. 2021a. Dense-Resolution Network for Point Cloud Classification and Segmentation. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2021).

[69]

Shi Qiu, Saeed Anwar, and Nick Barnes. 2021b. Geometric Back-projection Network for Point Cloud Classification. arXiv preprint arXiv:1911.12885 (2021).

[70]

Nicholas Sharp, Souhaib Attaiki, Keenan Crane, and Maks Ovsjanikov. 2021. Diffusion-Net: Discretization Agnostic Learning on Surfaces. ACM Trans. Graph. 41, 3 (2021), 27:1--27:16.

[71]

Yiru Shen, Chen Feng, Yaoqing Yang, and Dong Tian. 2018. Mining point cloud local structures by kernel correlation and graph pooling. CVPR (2018).

[72]

Martin Simonovsky and Nikos Komodakis. 2017. Dynamic edge-conditioned filters in convolutional neural networks on graphs. CVPR (2017).

[73]

Dmitriy Smirnov and Justin Solomon. 2021. HodgeNet: Learning Spectral Geometry on Triangle Meshes. ACM Trans. Graph. 40, 4 (2021), 166:1--166:11.

Digital Library

[74]

Xiao Sun, Zhouhui Lian, and Jianguo Xiao. 2019. SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation. ACM International Conference on Multimedia (2019), 980--988.

Digital Library

[75]

Gusi Te, Wei Hu, Amin Zheng, and Zongming Guo. 2018. RGCNN: Regularized graph CNN for point cloud segmentation. ACM International Conference on Multimedia (2018), 746--754.

Digital Library

[76]

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. ICCV (2019).

[77]

Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley. 2018. Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds. arXiv preprint arXiv:1802.08219 (2018).

[78]

Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Duc Thanh Nguyen, and Sai-Kit Yeung. 2019. Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data. ICCV (2019).

[79]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. NeurIPS (2017).

[80]

Chu Wang, Babak Samari, and Kaleem Siddiqi. 2018. Local spectral graph convolution for point set feature learning. ECCV (2018).

[81]

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), 146:1--146:12.

Digital Library

[82]

Max Wardetzky. 2006. Discrete Differential Operators on Polyhedral Surfaces - Convergence and Approximation. Ph.D. Dissertation. Freie Universiät Berlin.

[83]

Joachim Weickert. 1998. Anisotropic diffusion in image processing. Vol. 1.

[84]

Maurice Weiler, Patrick Forré, Erik Verlinde, and Max Welling. 2021. Coordinate Independent Convolutional Networks - Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. arXiv preprint arXiv:2106.06020 (2021).

[85]

Ruben Wiersma, Elmar Eisemann, and Klaus Hildebrandt. 2020. CNNs on surfaces using rotation-equivariant features. ACM Trans. Graph. 4 (2020), 92:1--92:12.

[86]

Wenxuan Wu, Zhongang Qi, and Li Fuxin. 2019. PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR (2019).

[87]

Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 2015. 3D ShapeNets: A deep representation for volumetric shapes. CVPR (2015).

[88]

Tiange Xiang, Chaoyi Zhang, Yang Song, Jianhui Yu, and Weidong Cai. 2021. Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis. ICCV (2021).

[89]

Mutian Xu, Runyu Ding, Hengshuang Zhao, and Xiaojuan Qi. 2021a. PAConv: Position Adaptive Convolution With Dynamic Kernel Assembling on Point Clouds. CVPR (2021).

[90]

Mutian Xu, Junhao Zhang, Zhipeng Zhou, Mingye Xu, Xiaojuan Qi, and Yu Qiao. 2021b. Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud. AAAI (2021).

[91]

Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao. 2018. SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters. ECCV (2018).

[92]

Jiancheng Yang, Qiang Zhang, B Ni, L Li, J Liu, Mengdie Zhou, and Q Tian. 2019. Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling. CVPR (2019).

[93]

Li Yi, Vladimir G. Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas. 2016. A Scalable Active Framework for Region Annotation in 3D Shape Collections. ACM Trans. Graph. 35, 6 (2016), 210:1--210:12.

Digital Library

[94]

Cheng Zhang, Haocheng Wan, Shengqiang Liu, Xinyi Shen, and Zizhao Wu. 2021. PVT: Point-Voxel Transformer for 3D Deep Learning. arXiv preprint arXiv:2108.06076 (2021).

[95]

Kuangen Zhang, Ming Hao, Jing Wang, Clarence W de Silva, and Chenglong Fu. 2019. Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features. arXiv preprint arXiv:1904.10014 (2019).

[96]

Yingxue Zhang and Michael Rabbat. 2018. A Graph-CNN for 3D point cloud classification. ICASSP (2018), 6279--6283.

[97]

Hengshuang Zhao, Li Jiang, Chi-Wing Fu, and Jiaya Jia. 2019. PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing. CVPR (2019).

[98]

Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip H.S. Torr, and Vladlen Koltun. 2021. Point Transformer. ICCV (2021).

Cited By

Kovács ÁHermosilla PRaidou R(2024)Surface‐aware Mesh Texture Synthesis with Pre‐trained 2D CNNsComputer Graphics Forum10.1111/cgf.1501643:2Online publication date: 23-Apr-2024
https://doi.org/10.1111/cgf.15016
Shakibajahromi BKim EBreen D(2024)RIMeshGNN: A Rotation-Invariant Graph Neural Network for Mesh Classification2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00312(3138-3148)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00312
Chen CWu YDai QZhou HXu MYang SHan XYu Y(2024)A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented PerspectiveIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.344546346:12(10297-10318)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3445463
Show More Cited By

Index Terms

DeltaConv: anisotropic operators for geometric deep learning on point clouds
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Shape analysis
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Recommendations

Neural Gradient Learning and Optimization for Oriented Point Normal Estimation
SA '23: SIGGRAPH Asia 2023 Conference Papers

We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation. It has excellent gradient approximation properties for the underlying geometry of the ...
Self-Sampling for Neural Point Cloud Consolidation
We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud. Unlike other point up-sampling methods which analyze shapes via local patches, in this work, we learn from global subsets. We repeatedly self-...
CurvaNet: Geometric Deep Learning based on Directional Curvature for 3D Shape Analysis
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Over the last decade, deep learning research has achieved tremendous success in computer vision and natural language processing. The current widely successful deep learning models are largely based on convolution and pooling operations on a Euclidean ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 41, Issue 4

July 2022

1978 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3528223

Issue’s Table of Contents

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 July 2022

Published in TOG Volume 41, Issue 4

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Google Cloud
NWO VIDI
TU Delft Universiteitsfonds
Indonesia Endowment Fund for Education (LPDP)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
1,149
Total Downloads

Downloads (Last 12 months)469
Downloads (Last 6 weeks)38

Reflects downloads up to 26 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kovács ÁHermosilla PRaidou R(2024)Surface‐aware Mesh Texture Synthesis with Pre‐trained 2D CNNsComputer Graphics Forum10.1111/cgf.1501643:2Online publication date: 23-Apr-2024
https://doi.org/10.1111/cgf.15016
Shakibajahromi BKim EBreen D(2024)RIMeshGNN: A Rotation-Invariant Graph Neural Network for Mesh Classification2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00312(3138-3148)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00312
Chen CWu YDai QZhou HXu MYang SHan XYu Y(2024)A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented PerspectiveIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.344546346:12(10297-10318)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3445463
Duan YSun HYan JLu JZhou J(2024)Learning Cross-Attention Point Transformer With Global Porous SamplingIEEE Transactions on Image Processing10.1109/TIP.2024.348661233(6283-6297)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3486612
Zhou WJin WWang QLiu KShe YYu YMa C(2024)Unveiling the Power of Dual Positional Encoding in Point Cloud Learning with DPAtteNet2024 IEEE 17th International Conference on Signal Processing (ICSP)10.1109/ICSP62129.2024.10846792(483-488)Online publication date: 28-Oct-2024
https://doi.org/10.1109/ICSP62129.2024.10846792
Stearns CFu ALiu JPark JRempe DPaschalidou DGuibas L(2024)CurveCloudNet: Processing Point Clouds with 1D Structure2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02643(27981-27991)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02643
Mitchel TEsteves CMakadia A(2024)Single Mesh Diffusion Models with Field Latents for Texture Generation2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00760(7953-7963)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00760
Li Z(2024)Adaptive Point Cloud Resampling Study Based on Unbalanced Optimal Transport2024 4th International Conference on Computer Science and Blockchain (CCSB)10.1109/CCSB63463.2024.10735524(552-555)Online publication date: 6-Sep-2024
https://doi.org/10.1109/CCSB63463.2024.10735524
Antić DTiwari GOzcomlekci BMarin RPons-Moll G(2024)CloSe: A 3D Clothing Segmentation Dataset and Model2024 International Conference on 3D Vision (3DV)10.1109/3DV62453.2024.00020(591-601)Online publication date: 18-Mar-2024
https://doi.org/10.1109/3DV62453.2024.00020
Viganò GMelzi S(2024)Bijective upsampling and learned embedding for point clouds correspondencesComputers and Graphics10.1016/j.cag.2024.103985122:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.cag.2024.103985
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Figures

Tables

Media

View Issue’s Table of Contents