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.
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
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?
RigNet's graph neural network builds upon and adapts the edge convolution operator proposed in [Wang et al. 2019] to perform edge convolutions within mesh-based and geodesic neighborhoods.
“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.
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 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
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 19,268 · see citing works
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759 · see citing works
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
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 12,099 · see citing works
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 · see citing works
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× · see citing works
“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 9,834 · see citing works
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 9,767 · see citing works
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 · see citing works
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 6,912 · see citing works
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 · see citing works
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 5,414 · see citing works
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 · see citing works
SGDR: Stochastic Gradient Descent with Warm Restarts
Loshchilov, I., & Hutter, F. (2016) · arXiv · doi:10.48550/arxiv.1608.03983 · Cited by 5,309 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Gated Graph Sequence Neural NetworksPreprint
Li, Y., Tarlow, D., Brockschmidt, M., & Zemel, R. (2015) · International Conference on Learning Representations · doi:10.48550/arxiv.1511.05493 · Cited by 3,735 · see citing works
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 3,674 · see citing works
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 · see citing works
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 3,515 · see citing works
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 · see citing works
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 · see citing works
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 2,410 · see citing works
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 · see citing works
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 · see citing works
Self-citationShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T. A., Guibas, L. J., Hanrahan, P., Qi-xing Huang, A., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2023) · doi:10.5281/zenodo.10089705 · Cited by 1,624 · see citing works
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 · see citing works
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 1,504 · see citing works
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 · see citing works
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 · see citing works
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
Simonovsky, M., & Nikos Komodakis, A. (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 1,346 · see citing works
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 · see citing works
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 · see citing works
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
Klokov, R., & Lempitsky, V. (2017) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.99 · Cited by 1,138 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 993 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Laplace-Beltrami eigenfunctions for deformation invariant shape representation
Rustamov, R. M. (2007) · doi:10.5555/1281991.1282022 · Cited by 555 · see citing works
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 553 · see citing works
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 · see citing works
Self-citationShape Context: A New Descriptor for Shape Matching and Object Recognition
Belongie, S., Malik, J., & Puzicha, J. (2000) · Cited by 510 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 450 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 280 · see citing works
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 · see citing works
Integral 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 234 · see citing works
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Monti, F., Bronstein, M. M., & Bresson, X. (2017) · neural information processing systems · Cited by 232 · see citing works
Self-citationDeformable 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 231 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 84 · see citing works
Self-citationPoint Cloud GANPreprint
Li, C., Zaheer, M., Zhang, Y., Póczos, B., & Salakhutdinov, R. (2018) · arXiv (Cornell University) · Cited by 62 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Neighbors Do Help: Deeply Exploiting Local Structures of Point Clouds
Shen, Y., Feng, C., Yang, Y. L., & Tian, D. (2017) · Cited by 24 · see citing works
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 · see citing works
Surface NetworksPreprint
Kostrikov, I., Jiang, Z., Panozzo, D., Zorin, D., & Bruna, J. (2017) · arXiv (Cornell University) · Cited by 3 · see citing works
Flagged first, then by citations
4 of the 90 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- 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.
- Diederik P. Kingma and Max Welling . 2013 . Auto-encoding variational bayes. arXiv:1312.6114 (2013). Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv:1312.6114 (2013). · arXiv
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 · see citing works
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 · see citing works
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 3,557 · see citing works
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 · see citing works
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 · see citing works
SuperGlue: Learning Feature Matching With Graph Neural Networks
Sarlin, P.-E., DeTone, D., Malisiewicz, T., & Rabinovich, A. (2020) · 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/cvpr42600.2020.00499 · Cited by 3,002 · see citing works
Zhao, H., Jiang, L., Jia, J., Torr, P. H. S., & Koltun, V. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01595 · Cited by 2,847 · see citing works
Fast Graph Representation Learning with PyTorch GeometricPreprint
Fey, M., & Lenssen, J. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02428 · Cited by 3,402 · see citing works
Deep Learning for 3D Point Clouds: A Survey
Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., & Bennamoun, M. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3005434 · Cited by 2,445 · see citing works
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Wang, X., & Li, H. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01054 · Cited by 2,341 · see citing works
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., & Markham, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01112 · Cited by 2,275 · see citing works
Guo, M.-H., Cai, J.-X., Liu, Z.-N., Mu, T.-J., Martin, R. R., & Hu, S.-M. (2021) · Computational Visual Media · doi:10.1007/s41095-021-0229-5 · Cited by 2,100 · see citing works
Self-citationSelf-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036 · see citing works
PointConv: Deep Convolutional Networks on 3D Point Clouds
Wu, W., Qi, Z., & Li, F. (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.2019.00985 · Cited by 1,972 · see citing works
Graph convolutional networks: a comprehensive review
Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019) · Computational Social Networks · doi:10.1186/s40649-019-0069-y · Cited by 1,888 · see citing works
DeepGCNs: Can GCNs Go As Deep As CNNs?
Li, G., Muller, M., Thabet, A., & Ghanem, B. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00936 · Cited by 1,540 · see citing works
A survey on deep learning techniques for image and video semantic segmentation
García-García, A., Orts‐Escolano, S., Oprea, S., Villena-Martínez, V., Martínez-González, P., & García‐Rodríguez, J. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.05.018 · Cited by 2,488 · see citing works
Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., & Grohe, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014602 · Cited by 172 · see citing works
Deep Hough Voting for 3D Object Detection in Point Clouds
Qi, C. R., Litany, O., He, K., & Guibas, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00937 · Cited by 1,244 · see citing works
Deep Closest Point: Learning Representations for Point Cloud Registration
Wang, Y., & Solomon, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00362 · Cited by 1,151 · see citing works
Self-citationYuan, W., Khot, T., Held, D., Mertz, C., & Hebert, M. (2018) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00088 · Cited by 1,068 · see citing works
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Liu, Y., Fan, B., Xiang, S., & Pan, C. (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.2019.00910 · Cited by 1,042 · see citing works
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
Shi, W., & Rajkumar, R. (2020) · 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/cvpr42600.2020.00178 · Cited by 979 · see citing works
TEASER: Fast and Certifiable Point Cloud Registration
Yang, H., Shi, J., & Carlone, L. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3033695 · Cited by 907 · see citing works
PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet
Aoki, Y., Goforth, H., Srivatsan, R. A., & Lucey, S. (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.2019.00733 · Cited by 901 · see citing works
PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing
Zhao, H., Jiang, L., Fu, C., & Jia, 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.2019.00571 · Cited by 898 · see citing works
Uy, M. A., Pham, Q.-H., Hua, B., Nguyen, T., & Yeung, S.-K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00167 · Cited by 891 · see citing works
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
Yu, X., Tang, L., Rao, Y., Huang, T., Zhou, J., & Lu, J. (2022) · 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/cvpr52688.2022.01871 · Cited by 832 · see citing works
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Tang, H., Liu, Z., Zhao, S., Lin, Y., Lin, J., Wang, H., & Han, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58604-1_41 · Cited by 781 · see citing works
Self-citationMeshCNN: a network with an edge
Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., & Cohen-Or, D. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322959 · Cited by 742 · see citing works
PREDATOR: Registration of 3D Point Clouds with Low Overlap
Huang, S., Gojčič, Ž., Usvyatsov, M., Wieser, A., & Schindler, K. (2021) · 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/cvpr46437.2021.00425 · Cited by 731 · see citing works
A Survey on 3D Object Detection Methods for Autonomous Driving Applications
Arnold, E. H., Al-Jarrah, O. Y., Dianati, M., Fallah, S., Oxtoby, D., & Mouzakitis, A. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2892405 · Cited by 720 · see citing works
PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling
Yan, X., Zheng, C., Li, Z., Wang, S., & Cui, S. R. (2020) · 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/cvpr42600.2020.00563 · Cited by 709 · see citing works
Hamilton, W. L. (2020) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01588-5 · Cited by 687 · see citing works
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping
Fang, H.-S., Wang, C., Gou, M., & Lu, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01146 · Cited by 683 · see citing works
GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition
Chao, H., He, Y., Zhang, J., & Feng, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018126 · Cited by 681 · see citing works
Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks
Guo, M.-H., Liu, Z.-N., Mu, T., & Hu, S. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3211006 · Cited by 681 · see citing works
Self-citationAdversarial Attacks and Defenses in Deep Learning
Ren, K., Zheng, T., Qin, Z., & Liu, X. (2020) · Engineering · doi:10.1016/j.eng.2019.12.012 · Cited by 678 · see citing works
Mo, K., Zhu, S., Chang, A. X., Yi, L., Tripathi, S., Guibas, L. J., & Su, H. (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.2019.00100 · Cited by 666 · see citing works
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
Xie, S., Gu, J., Guo, D., Qi, C. R., Guibas, L. J., & Litany, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_34 · Cited by 655 · see citing works
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
Zhu, X., Zhou, H., Wang, T., Hong, F., Ma, Y., Li, W., Li, H., & Lin, D. (2021) · 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/cvpr46437.2021.00981 · Cited by 647 · see citing works
RPM-Net: Robust Point Matching Using Learned Features
Yew, Z. J., & Lee, G. H. (2020) · 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/cvpr42600.2020.01184 · Cited by 640 · see citing works
Diffusion Probabilistic Models for 3D Point Cloud Generation
Luo, S., & Hu, W. (2021) · 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/cvpr46437.2021.00286 · Cited by 635 · see citing works
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
Li, Y., Ma, L., Zhong, Z., Liu, F., Chapman, M. A., Cao, D., & Li, J. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015992 · Cited by 619 · see citing works
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
Zhang, Y., Zhou, Z., David, P., Yue, X., Xi, Z., Gong, B., & Foroosh, H. (2020) · 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/cvpr42600.2020.00962 · Cited by 599 · see citing works
Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT
Chen, Z., Chen, D., Zhang, X., Yuan, Z., & Cheng, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3100509 · Cited by 613 · see citing works
Masked Autoencoders for Point Cloud Self-supervised Learning
Pang, Y., Wang, W., Tay, F. E. H., Liu, W., Tian, Y., & Yuan, L. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20086-1_35 · Cited by 605 · see citing works
ABC: A Big CAD Model Dataset for Geometric Deep Learning
Koch, S., Matveev, A., Jiang, Z., Williams, F. E., Artemov, A., Burnaev, E. V., Alexa, M., Zorin, D., & Panozzo, D. (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.2019.00983 · Cited by 587 · see citing works
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
Mousavian, A., Eppner, C., & Fox, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00299 · Cited by 585 · see citing works
Stratified Transformer for 3D Point Cloud Segmentation
Lai, X., Liu, J., Jiang, L., Wang, L., Zhao, H., Liu, S., Qi, X., & Jia, J. (2022) · 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/cvpr52688.2022.00831 · Cited by 576 · see citing works
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
Yu, X., Rao, Y., Wang, Z., Liu, Z., Lu, J., & Zhou, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01227 · Cited by 602 · see citing works
Self-citationPAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds
Xu, M., Ding, R., Zhao, H., & Qi, X. (2021) · 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/cvpr46437.2021.00319 · Cited by 560 · see citing works
Shi, S., Jiang, L., Deng, J., Wang, Z., Guo, C., Shi, J., Wang, X., & Li, H. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01710-9 · Cited by 571 · see citing works
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and GaugesPreprint
Bronstein, M. M., Bruna, J., Cohen, T., & Veličković, P. (2021) · arXiv (Cornell University) · doi:10.4230/oasics.dx.2026.1 · Cited by 928 · see citing works
Self-citationDepGraph: Towards Any Structural Pruning
Fang, G., Ma, X., Song, M., Mi, M. B., & Wang, X. (2023) · 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/cvpr52729.2023.01544 · Cited by 538 · see citing works
Local Implicit Grid Representations for 3D Scenes
Jiang, C. M., Sud, A., Makadia, A., Huang, J., NieBner, M., & Funkhouser, T. A. (2020) · 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/cvpr42600.2020.00604 · Cited by 531 · see citing works
PU-GAN: A Point Cloud Upsampling Adversarial Network
Li, R., Li, X., Fu, C., Cohen‐Or, D., & Heng, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00730 · Cited by 525 · see citing works
FlowNet3D: Learning Scene Flow in 3D Point Clouds
Liu, X., Qi, C. R., & Guibas, L. 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.2019.00062 · Cited by 518 · see citing works
Machine Learning Methods for Small Data Challenges in Molecular Science
Dou, B., Zhu, Z., Merkurjev, E., Ke, L., Chen, L., Jian, J., Zhu, Y., Liu, J., Zhang, B., & Wei, G. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00189 · Cited by 522 · see citing works
G-TAD: Sub-Graph Localization for Temporal Action Detection
Xu, M., Zhao, C., Rojas, D. S., Thabet, A., & Ghanem, B. (2020) · 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/cvpr42600.2020.01017 · Cited by 503 · see citing works
An End-to-End Transformer Model for 3D Object Detection
Misra, I., Girdhar, R., & Joulin, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00290 · Cited by 506 · see citing works
3D Object Detection with Pointformer
Pan, X., Xia, Z., Song, S., Li, L. E., & Huang, G. M. (2021) · 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/cvpr46437.2021.00738 · Cited by 493 · see citing works
Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation
Xie, Y., Tian, J., & Zhu, X. X. (2020) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2019.2937630 · Cited by 522 · see citing works
HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction
Zhou, Z., Ye, L., Wang, J., Wu, K., & Lu, K. (2022) · 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/cvpr52688.2022.00862 · Cited by 490 · see citing works
Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling
Yang, J., Zhang, Q., Ni, B., Li, L., Liu, J., Zhou, M., & Tian, Q. (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.2019.00344 · Cited by 465 · see citing works
PointCLIP: Point Cloud Understanding by CLIP
Zhang, R., Guo, Z., Zhang, W., Li, K., Miao, X., Cui, B., Qiao, Y., Gao, P., & Li, H. (2022) · 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/cvpr52688.2022.00836 · Cited by 483 · see citing works
3D Object Detection for Autonomous Driving: A Comprehensive Survey
Mao, J., Shi, S., Wang, X., & Li, H. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01790-1 · Cited by 492 · see citing works
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
Jiang, L., Zhao, H., Shi, S., Liu, S., Fu, C., & Jia, J. (2020) · 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/cvpr42600.2020.00492 · Cited by 467 · see citing works
3D Object Detection for Autonomous Driving: A Survey
Qian, R., Lai, X., & Li, X. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108796 · Cited by 471 · see citing works
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
Xu, C., Wu, B., Wang, Z., Zhan, W., Vajda, P., Keutzer, K., & Tomizuka, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58604-1_1 · Cited by 436 · see citing works
ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics
Zhang, Z., Hua, B., & Yeung, S.-K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00169 · Cited by 428 · see citing works
GRNet: Gridding Residual Network for Dense Point Cloud Completion
Xie, H., Yao, H., Zhou, S., Mao, J., Zhang, S., & Sun, W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_21 · Cited by 430 · see citing works
Jet tagging via particle clouds
Qu, H., & Gouskos, L. (2020) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.101.056019 · Cited by 430 · see citing works
3D Shape Generation and Completion through Point-Voxel Diffusion
Zhou, L., Du, Y., & Wu, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00577 · Cited by 421 · see citing works
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds
Zhang, Y., Hu, Q., Xu, G., Ma, Y., Wan, J., & Guo, Y. (2022) · 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/cvpr52688.2022.01838 · Cited by 420 · see citing works
Deep Snake for Real-Time Instance Segmentation
Peng, S., Jiang, W., Pi, H., Li, X., Bao, H., & Zhou, X. (2020) · 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/cvpr42600.2020.00856 · Cited by 412 · see citing works
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors
Deng, H., Birdal, T., & Ilić, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_37 · Cited by 406 · see citing works
Point-Based Multi-View Stereo Network
Chen, R., Han, S., Xu, J. L., & Su, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00162 · Cited by 411 · see citing works
Chen, Y., Liu, S., Shen, X., & Jia, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00987 · Cited by 406 · see citing works
Dynamic Hypergraph Neural Networks
Jiang, J., Wei, Y., Feng, Y., Cao, J., & Gao, Y. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/366 · Cited by 402 · see citing works
Graph Matching Networks for Learning the Similarity of Graph Structured ObjectsPreprint
Li, Y., Gu, C., Dullien, T., Vinyals, O., & Kohli, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.12787 · Cited by 412 · see citing works
Real-time monitoring of construction sites: Sensors, methods, and applications
Rao, A. S., Radanović, M., Liu, Y., Hu, S., Fang, Y., Khoshelham, K., Palaniswami, M. S., & Ngo, T. (2022) · Automation in Construction · doi:10.1016/j.autcon.2021.104099 · Cited by 425 · see citing works
Fake news detection on social media using geometric deep learning
Monti, F., Frasca, F., Eynard, D., Mannion, D., & Bronstein, M. M. (2019) · arXiv · doi:10.48550/arxiv.1902.06673 · Cited by 392 · see citing works
Self-citationReview: Deep Learning on 3D Point Clouds
Bello, S. A., Yu, S., Wang, C., Adam, J. M., & Li, J. (2020) · Remote Sensing · doi:10.3390/rs12111729 · Cited by 399 · see citing works
PointCleanNet : Learning to Denoise and Remove Outliers from Dense Point Clouds
Rakotosaona, M., Barbera, V. L., Guerrero, P., Mitra, N. J., & Ovsjanikov, M. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13753 · Cited by 381 · see citing works
Inductive Representation Learning on Temporal GraphsPreprint
Xu, D., Ruan, C., Körpeoğlu, E., Kumar, S., & Achan, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07962 · Cited by 565 · see citing works
Mutual Graph Learning for Camouflaged Object Detection
Zhai, Q., Li, X., Yang, F., Chen, C., Cheng, H., & Fan, D.-P. (2021) · 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/cvpr46437.2021.01280 · Cited by 369 · see citing works
Shen, Y., Shi, Y., Zhang, J., & Letaief, K. B. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3036965 · Cited by 367 · see citing works
Zhao, Y., Birdal, T., Deng, H., & Tombari, F. (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.2019.00110 · Cited by 363 · see citing works
Graph Neural Networks for Intelligent Transportation Systems: A Survey
Rahmani, S., Baghbani, A., Bouguila, N., & Patterson, Z. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3257759 · Cited by 371 · see citing works
Complex-YOLO: An Euler-Region-Proposal for Real-Time 3D Object Detection on Point Clouds
Simón, M., Milz, S., Amende, K., & Groß, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_11 · Cited by 360 · see citing works
Morphing and Sampling Network for Dense Point Cloud Completion
Liu, M., Sheng, L., Yang, S. Y., Shao, J., & Hu, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6827 · Cited by 350 · see citing works
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation
Xu, J., Zhang, R., Dou, J., Yushi, Z., Sun, J., & Pu, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01572 · Cited by 350 · see citing works
A Comprehensive Survey on Deep Graph Representation Learning
Ju, W., Fang, Z., Gu, Y., Liu, Z., Long, Q., Qiao, Z., Qin, Y., Shen, J., Sun, F., Xiao, Z., et al. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106207 · Cited by 359 · see citing works
Self-citationPoint-Voxel CNN for Efficient 3D Deep LearningPreprint
Liu, Z., Tang, H., Lin, Y., & Han, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03739 · Cited by 379 · see citing works
Self-citationLPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis
Liu, Z., Zhou, S., Suo, C., Yin, P., Chen, W., Wang, H., Li, H., & Liu, Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00292 · Cited by 336 · see citing works
Self-citationPUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling
Liu, H., Yuan, H., Hou, J.-H., Hamzaoui, R., & Gao, W. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3222918 · Cited by 136 · see citing works
Generating 3D Adversarial Point Clouds
Xiang, C., Qi, C. R., & Li, B. (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.2019.00935 · Cited by 338 · see citing works
PRNet: Self-Supervised Learning for Partial-to-Partial RegistrationPreprint
Wang, Y., & Solomon, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12240 · Cited by 336 · see citing works
Self-citationFUTR3D: A Unified Sensor Fusion Framework for 3D Detection
Chen, X., Zhang, T., Wang, Y., Wang, Y., & Zhao, H. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00022 · Cited by 337 · see citing works
Self-citation
- 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 · 2018: 37 · 2012: 1
- 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 › Engineering › Computational Mechanics › 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
