The DOI matches its Crossref record; 87 of the 90 references it cites were found in a registry or an index.
Dynamic Graph CNN for Learning on Point Clouds
Open accessAbstract
Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Our method instead learns edge feature representations within both mesh and geodesic neighborhoods, and combines them into vertex representations inspired by the edge convolution scheme of [Wang et al. 2019].”
From RigNet: Neural Rigging for Articulated Characters (2020) · cites this paper as Wang et al. 2019
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Graph Attention NetworksPreprint
Veličković, P., Casanova, A., Liò, P., Cucurull, G., Romero, A., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.17863/cam.48429 · Cited by 19,745
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596 · cited 1×
“a connection to existing work, Non-local Neural Networks [Wang et al. 2018a] explored similar ideas in the video recognition field, and follow-up work by Xie et al.”
MethodPointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric SpacePreprint
Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02413 · Cited by 10,099
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R., Su, H., Mo, K., & Guibas, L. J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.16 · Cited by 10,029
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
Advances In Deep Learning On Graphs (Gsp'18 Workshop)Preprint
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.09375 · Cited by 7,021
International Conference on Learning Representations (ICLR 2013)
松尾, 豊. (2013) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 6,286
Neural Message Passing for Quantum ChemistryPreprint
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,678
3D ShapeNets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., & Xiao, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298801 · Cited by 5,413
SGDR: Stochastic Gradient Descent with Warm Restarts
Loshchilov, I., & Hutter, F. (2016) · arXiv · doi:10.48550/arxiv.1608.03983 · Cited by 5,309
Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., & Vandergheynst, P. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2235192 · Cited by 4,421
Spectral Networks and Locally Connected Networks on Graphs
Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6203 · Cited by 3,983
Fast Point Feature Histograms (FPFH) for 3D registration
Rusu, R. B., Blodow, N., & Beetz, M. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152473 · Cited by 3,977
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Maturana, D., & Scherer, S. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353481 · Cited by 3,752
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661
Self-citationMulti-view Convolutional Neural Networks for 3D Shape Recognition
Su, H., Maji, S., Kalogerakis, E., & Miller, E. G. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.114 · Cited by 3,608
Gated Graph Sequence Neural NetworksPreprint
Li, Y., Tarlow, D., Brockschmidt, M., & Zemel, R. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05493 · Cited by 3,329
Using spin images for efficient object recognition in cluttered 3D scenes
Johnson, A., & Hebert, M. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.765655 · Cited by 2,656
Frustum PointNets for 3D Object Detection from RGB-D Data
Qi, C. R., Liu, W., Wu, C., Su, H., & Guibas, L. J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00102 · Cited by 2,655
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Fan, H., Su, H., & Guibas, L. J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.264 · Cited by 2,513
3D Semantic Parsing of Large-Scale Indoor Spaces
Armeni, I., Şener, O., Zamir, A., Jiang, H., Brilakis, I., Fischer, M. A., & Savarese, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.170 · Cited by 1,897
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., & Bronstein, M. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.576 · Cited by 1,820
Self-citationShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T. A., Guibas, L. J., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2023) · doi:10.5281/zenodo.10089705 · Cited by 1,774
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Zhu, Y., Mottaghi, R., Kolve, E., Lim, J. J., Gupta, A., Fei-Fei, L., & Farhadi, A. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989381 · Cited by 1,533
Volumetric and Multi-view CNNs for Object Classification on 3D Data
Qi, C. R., Su, H., NieBner, M., Dai, A., Yan, M., & Guibas, L. J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.609 · Cited by 1,527
A Concise and Provably Informative Multi‐Scale Signature Based on Heat Diffusion
Sun, J. Z., Ovsjanikov, M., & Guibas, L. J. (2009) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2009.01515.x · Cited by 1,466
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
Simonovsky, M., & Komodakis, N. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.11 · Cited by 1,391
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
Yang, Y. L., Feng, C., Shen, Y., & Tian, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00029 · Cited by 1,365
Deep Convolutional Networks on Graph-Structured Data
Henaff, M., Bruna, J., & LeCun, Y. (2015) · arXiv · doi:10.48550/arxiv.1506.05163 · Cited by 1,255
A scalable active framework for region annotation in 3D shape collections
Yi, L., Kim, V. G., Ceylan, D., Shen, I., Yan, M., Su, H., Lu, C., Qi-xing Huang, A., Sheffer, A., & Guibas, L. J. (2016) · ACM Transactions on Graphics · doi:10.1145/2980179.2980238 · Cited by 1,209
Towards 3D Point cloud based object maps for household environments
Rusu, R. B., Márton, Z.-C., Blodow, N., Dolha, M. E., & Beetz, M. (2008) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.08.005 · Cited by 1,087
Aligning point cloud views using persistent feature histograms
Rusu, R. B., Blodow, N., Márton, Z.-C., & Beetz, M. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4650967 · Cited by 1,087
Shape Classification Using the Inner-Distance
Ling, H., & Jacobs, D. W. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.41 · Cited by 1,069
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
Klokov, R., & Lempitsky, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.99 · Cited by 1,043
Deep Continuous Fusion for Multi-sensor 3D Object Detection
Liang, M., Yang, B., Wang, S., & Urtasun, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_39 · Cited by 1,005
Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., Maaten, L. V. D., Yuille, A. L., & He, K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00059 · Cited by 940
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M., & Kautz, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00268 · Cited by 804
The wave kernel signature: A quantum mechanical approach to shape analysis
Aubry, M., Schlickewei, U., & Cremers, D. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130444 · Cited by 747
Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs
Tatarchenko, M., Dosovitskiy, A., & Brox, T. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.230 · Cited by 740
Ovsjanikov, M., Ben‐Chen, M., Solomon, J., Butscher, A., & Guibas, L. J. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185526 · Cited by 736
Self-citationGeodesic Convolutional Neural Networks on Riemannian Manifolds
Masci, J., Boscaini, D., Bronstein, M. M., & Vandergheynst, P. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.112 · Cited by 721
Self-citationScale-invariant heat kernel signatures for non-rigid shape recognition
Bronstein, M. M., & Kokkinos, I. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539838 · Cited by 690
Self-citationCayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters
Levie, R., Monti, F., Bresson, X., & Bronstein, M. M. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2879624 · Cited by 685
Self-citationA Survey on Shape Correspondence
van Kaick, O. M., Zhang, H., Hamarneh, G., & Cohen‐Or, D. (2011) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2011.01884.x · Cited by 606
3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey
Guo, Y., Bennamoun, M., Sohel, F. A., Lu, M., & Wan, J. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2316828 · Cited by 602
Generative and Discriminative Voxel Modeling with Convolutional Neural NetworksPreprint
Brock, A., Lim, T., Ritchie, J. M., & Weston, N. J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.04236 · Cited by 602
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Shen, Y., Feng, C., Yang, Y. L., & Tian, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00478 · Cited by 561
Point convolutional neural networks by extension operators
Atzmon, M., Maron, H., & Lipman, Y. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201301 · Cited by 559
Laplace-Beltrami eigenfunctions for deformation invariant shape representation
Rustamov, R. M. (2007) · doi:10.5555/1281991.1282022 · Cited by 555
Learning shape correspondence with anisotropic convolutional neural networksPreprint
Boscaini, D., Masci, J., Rodolà, E., & Bronstein, M. (2016) · Neural Information Processing Systems · doi:10.48550/arxiv.1605.06437 · Cited by 540
Self-citationShape Context: A New Descriptor for Shape Matching and Object Recognition
Belongie, S., Malik, J., & Puzicha, J. (2000) · Cited by 511
Deep Parametric Continuous Convolutional Neural Networks
Wang, S., Suo, S., Ma, W.-C., Pokrovsky, A., & Urtasun, R. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00274 · Cited by 470
SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
Yi, L., Su, H., Guo, X., & Guibas, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.697 · Cited by 461
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
Fey, M., Lenssen, J. E., Weichert, F., & Muller, H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00097 · Cited by 459
Shape-based recognition of 3D point clouds in urban environments
Golovinskiy, A., Kim, V. G., & Funkhouser, T. A. (2009) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459471 · Cited by 429
PCPN et Learning Local Shape Properties from Raw Point Clouds
Guerrero, P., Kleiman, Y., Ovsjanikov, M., & Mitra, N. J. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13343 · Cited by 387
A combined texture-shape descriptor for enhanced 3D feature matching
Tombari, F., Salti, S., & Stefano, L. D. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116679 · Cited by 313
Deep Learning 3D Shape Surfaces Using Geometry Images
Sinha, A., Bai, J., & Karthik Ramani, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_14 · Cited by 299
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence
Litany, O., Remez, T., Rodolà, E., Bronstein, A. M., & Bronstein, M. M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.603 · Cited by 298
Self-citationExploring Spatial Context for 3D Semantic Segmentation of Point Clouds
Engelmann, F., Kontogianni, T., Hermans, A., & Leibe, B. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.90 · Cited by 287
Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds
Engelmann, F., Kontogianni, T., Hermans, A., & Leibe, B. (2017) · RWTH Publications (RWTH Aachen) · Cited by 254
Deformable Shape Completion with Graph Convolutional Autoencoders
Litany, O., Bronstein, A. M., Bronstein, M. M., & Makadia, A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00202 · Cited by 236
Self-citationIntegral Invariants for Shape Matching
Manay, S., Cremers, D., Hong, B., Yezzi, A. J., & Soatto, S. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.208 · Cited by 236
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Monti, F., Bronstein, M. M., & Bresson, X. (2017) · neural information processing systems · Cited by 232
Self-citationConvolutional neural networks on surfaces via seamless toric covers
Maron, H., Galun, M., Aigerman, N., Trope, M., Dym, N., Yumer, E., Kim, V. G., & Lipman, Y. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073616 · Cited by 227
Persistent Point Feature Histograms for 3D Point Clouds
Bogdan, R. R., Csaba, M. Z., Blodow, N., & Beetz, M. (2008) · IOS Press eBooks · doi:10.3233/978-1-58603-887-8-119 · Cited by 221
Boscaini, D., Masci, J., Melzi, S., Bronstein, M. M., Castellani, U., & Vandergheynst, P. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12693 · Cited by 205
Self-citationDense Human Body Correspondences Using Convolutional Networks
Wei, L., Qi-xing Huang, A., Ceylan, D., Vouga, E., & Li, H. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.171 · Cited by 184
Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment
Biasotti, S., Cerri, A., Bronstein, A. M., & Bronstein, M. M. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12734 · Cited by 131
Self-citationMOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS
Monti, F., Otness, K., & Bronstein, M. M. (2018) · IEEE Data Science Workshop (DSW) · doi:10.1109/dsw.2018.8439897 · Cited by 125
Self-citationGWCNN: A Metric Alignment Layer for Deep Shape Analysis
Ezuz, D., Solomon, J., Kim, V. G., & Ben‐Chen, M. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13244 · Cited by 86
Self-citationPoint Cloud GANPreprint
Li, C., Zaheer, M., Zhang, Y., Póczos, B., & Salakhutdinov, R. (2018) · arXiv (Cornell University) · Cited by 62
Kostrikov, I., Jiang, Z., Panozzo, D., Zorin, D., & Bruna, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · Cited by 43
Self-supervised Learning of Dense Shape CorrespondencePreprint
Halimi, O., Litany, O., Rodolà, E., Bronstein, A. M., & Kimmel, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.02415 · Cited by 32
Recognizing Objects in 3D Point Clouds with Multi-Scale Local Features
Lu, M., Guo, Y., Zhang, J., Ma, Y., & Lei, Y. (2014) · Sensors · doi:10.3390/s141224156 · Cited by 29
Generating 3D faces using Convolutional Mesh AutoencodersPreprint
Ranjan, A., Bolkart, T., Sanyal, S., & Black, M. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.10267 · Cited by 28
Neighbors Do Help: Deeply Exploiting Local Structures of Point Clouds
Shen, Y., Feng, C., Yang, Y. L., & Tian, D. (2017) · Cited by 24
3D-Div: A novel local surface descriptor for feature matching and pairwise range image registration
Shah, S. A. A., Bennamoun, M., Boussaïd, F., & El-Sallam, A. A. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738604 · Cited by 22
Surface NetworksPreprint
Kostrikov, I., Jiang, Z., Panozzo, D., Zorin, D., & Bruna, J. (2017) · arXiv (Cornell University) · Cited by 3
Research works printed but not linked: 2
- Michaël Defferrard , Xavier Bresson , and Pierre Vandergheynst . 2016 . Convolutional neural networks on graphs with fast localized spectral filtering . In Proceedings of the NIPS. Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Proceedings of the NIPS.
- Ian Goodfellow , Jean Pouget-Abadie , Mehdi Mirza , Bing Xu , David Warde-Farley , Sherjil Ozair , Aaron Courville , and Yoshua Bengio . 2014 . Generative adversarial nets . In Proceedings of the NIPS. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the NIPS.
Cited by
Sorted by citations
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880
Self-citationDeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00025 · Cited by 3,789
KPConv: Flexible and Deformable Convolution for Point Clouds
Thomas, H., Qi, C. R., Deschaud, J., Marcotegui, B., Goulette, F., & Guibas, L. J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00651 · Cited by 3,400
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. (2020) · UTS ePRESS (University of Technology Sydney) · Cited by 3,174
Metadata
- Authors
- Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M.
- Affiliations
- Massachusetts Institute of Technology (US)
- International Computer Science Institute (US)
- Imperial College London (GB)
- Venue
- ACM Transactions on Graphics
- Published
- 2019-10-10
- Volume
- 38
- Issue
- 5
- Pages
- 1-12
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2979750740
- Semantic Scholar
- e1799aaf23c12af6932dc0ef3dfb1638f01413d1
- Cited by
- 7,199
- References
- 90
- Citations by year
- 2026: 865 · 2025: 1221 · 2024: 1301 · 2023: 1160 · 2022: 924 · 2021: 887 · 2020: 570 · 2019: 233
- FWCI
- 443.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Graphics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- Air Force Office of Scientific Research (FA9550-19-1-0319)
- Toyota-CSAIL Joint Research Center
- National Science Foundation (IIS-1838071)
- Amazon Research Award
- MITIBM Watson AI Laboratory
- Skoltech-MIT Next Generation Program
- Army Research Office (W911NF-12-R-0011)
- ERC Consolidator (724228 (LEMAN))
- Google Faculty Research Award
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- point clouds, dynamic graph convolution, graph neural networks, 3D shape classification, 3D shape segmentation, point cloud topology, local neighborhood information, 3D object recognition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
