SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation

Going deeper with convolutions

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Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works

Multi-scale context aggregation by dilated convolutions

Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738 · 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

Spatial transformer networks

Jaderberg, M., Simonyan, K., Zisserman, A., & Kavukcuoglu, K. (2015) · arXiv · doi:10.48550/arxiv.1506.02025 · Cited by 5,137 · see citing works

Advances In Deep Learning On Graphs (Gsp'18 Workshop)

Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,977 · 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

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

Convolutional Networks on Graphs for Learning Molecular Fingerprints

Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628 · see citing works

ShapeNet: An Information-Rich 3D Model Repository

Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2015) · arXiv · doi:10.48550/arxiv.1512.03012 · Cited by 2,330 · see citing works

ShapeNet: 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

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

Functional maps

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

Geodesic 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

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

Learning 3D mesh segmentation and labeling

Kalogerakis, E., Hertzmann, A., & Singh, K. S. (2010) · ACM Transactions on Graphics · doi:10.1145/1778765.1778839 · Cited by 463 · see citing works

Vertex-frequency analysis on graphs

Shuman, D. I., Ricaud, B., & Vandergheynst, P. (2016) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2015.02.005 · Cited by 349 · see citing works

Angular synchronization by eigenvectors and semidefinite programming

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Learning part-based templates from large collections of 3D shapes

Kim, V. G., Li, W., Mitra, N. J., Chaudhuri, S., DiVerdi, S., & Funkhouser, T. A. (2013) · ACM Transactions on Graphics · doi:10.1145/2461912.2461933 · Cited by 224 · see citing works

Segmentation of 3D meshes through spectral clustering

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3D Mesh Labeling via Deep Convolutional Neural Networks

Guo, K., Zou, D., & Chen, X. (2015) · ACM Transactions on Graphics · doi:10.1145/2835487 · Cited by 214 · see citing works

Learning class‐specific descriptors for deformable shapes using localized spectral convolutional networks

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

Functional map networks for analyzing and exploring large shape collections

Qi-xing Huang, A., Wang, F., & Guibas, L. J. (2014) · ACM Transactions on Graphics · doi:10.1145/2601097.2601111 · Cited by 171 · see citing works

Image Co-segmentation via Consistent Functional Maps

Wang, F., Huang, Q., & Guibas, L. (2013) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.110 · Cited by 141 · see citing works

Mesh Segmentation via Spectral Embedding and Contour Analysis

Liu, R., & Zhang, H. (2007) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2007.01061.x · Cited by 125 · see citing works

Shape2Pose: human-centric shape analysis

Kim, V. G., Chaudhuri, S., Guibas, L., & Funkhouser, T. (2014) · ACM Transactions on Graphics · doi:10.1145/2601097.2601117 · Cited by 112 · see citing works

3D Shape Segmentation and Labeling via Extreme Learning Machine

Xie, Z., Xu, K., Liu, L., & Xiong, Y. (2014) · Computer Graphics Forum · doi:10.1111/cgf.12434 · Cited by 95 · see citing works

Fine-grained semi-supervised labeling of large shape collections

Qi-xing Huang, A., Su, H., & Guibas, L. J. (2013) · ACM Transactions on Graphics · doi:10.1145/2508363.2508364 · Cited by 74 · see citing works

Unsupervised Multi-class Joint Image Segmentation

Wang, F., Qi-xing Huang, A., Ovsjanikov, M., & Guibas, L. J. (2014) · 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.2014.402 · Cited by 57 · see citing works

Interactive shape co-segmentation via label propagation

Wu, Z., Shou, R., Wang, Y., & Liu, X. (2013) · Computers & Graphics · doi:10.1016/j.cag.2013.11.009 · Cited by 42 · see citing works

Learning 3D Part Detection from Sparsely Labeled Data

Makadia, A., & Yümer, M. E. (2014) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2014.108 · Cited by 10 · see citing works

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

PointNet++: 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

Dynamic Graph CNN for Learning on Point Clouds

Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199 · 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

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

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

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

3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

Graham, B., Engelcke, M., & Maaten, L. V. D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00961 · Cited by 1,788 · see citing works

Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images

Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., & Jiang, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_4 · Cited by 1,362 · 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

O-CNN

Wang, P.-S., Liu, Y., Guo, Y.-X., Sun, C.-Y., & Tong, X. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073608 · Cited by 1,031 · see citing works

SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters

Xu, Y., Fan, T., Xu, M., Zeng, L., & Qiao, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_6 · Cited by 867 · see citing works

Neural 3D Mesh Renderer

Kato, H., Ushiku, Y., & Harada, T. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00411 · Cited by 954 · 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

Graph Attention Convolution for Point Cloud Semantic Segmentation

Wang, L., Huang, Y., Hou, Y., Zhang, S., & Shan, 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.2019.01054 · Cited by 782 · see citing works

MeshCNN: 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

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

PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding

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

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

Tangent Convolutions for Dense Prediction in 3D

Tatarchenko, M., Park, J., Koltun, V., & Zhou, Q.-Y. (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.2018.00409 · Cited by 592 · 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

Generating 3D Faces Using Convolutional Mesh Autoencoders

Ranjan, A., Bolkart, T., Sanyal, S., & Black, M. J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_43 · Cited by 570 · see citing works

PAConv: 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

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

Explainability Methods for Graph Convolutional Neural Networks

Pope, P. E., Kolouri, S., Rostami, M., Martin, C. E., & Hoffmann, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01103 · Cited by 553 · see citing works

Recurrent Slice Networks for 3D Segmentation of Point Clouds

Huang, Q., Wang, W., & Neumann, U. (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.2018.00278 · Cited by 500 · see citing works

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Bhatti, U. A., Tang, H., Wu, G., Marjan, S., & Hussain, A. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/8342104 · Cited by 481 · 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

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

PointGrid: A Deep Network for 3D Shape Understanding

Le, T., & Duan, Y. (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.2018.00959 · 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

Local Spectral Graph Convolution for Point Set Feature Learning

Wang, C., Samari, B., & Kaleem Siddiqi, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_4 · Cited by 400 · 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

PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic SegmentationPreprint

Jiang, M., Wu, Y., Zhao, T., Zhao, Z., & Lu, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00652 · Cited by 338 · see citing works

GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud

Yi, L., Zhao, W., Wang, H., Sung, M., & 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.2019.00407 · Cited by 323 · see citing works

VV-Net: Voxel VAE Net With Group Convolutions for Point Cloud Segmentation

Meng, H.-Y., Gao, L., Lai, Y., & Manocha, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00859 · Cited by 322 · see citing works

RGCNN: Regularized Graph CNN for Point Cloud Segmentation

Te, G., Hu, W., Zheng, A., & Guo, Z. (2018) · Proceedings of the 26th ACM international conference on Multimedia · doi:10.1145/3240508.3240621 · Cited by 311 · see citing works

A-CNN: Annularly Convolutional Neural Networks on Point Clouds

Komarichev, A., Zhong, Z., & Hua, 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.2019.00760 · Cited by 306 · see citing works

DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing

Liu, Y., Fan, B., Meng, G., Lu, J., Xiang, S., & Pan, C. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00534 · Cited by 306 · see citing works

ConvPoint: Continuous convolutions for point cloud processing

Boulch, A. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.02.005 · Cited by 304 · see citing works

A Review of Deep Learning-Based Semantic Segmentation for Point Cloud

Zhang, J., Zhao, X., Chen, Z., & Lu, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2958671 · Cited by 304 · see citing works

Spectral Clustering with Graph Neural Networks for Graph PoolingPreprint

Bianchi, F. M., Grattarola, D., & Alippi, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.00481 · Cited by 304 · see citing works

Exploring 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

StructureNet

Mo, K., Guerrero, P., Yi, L., Su, H., Wonka, P., Mitra, N. J., & Guibas, L. J. (2019) · ACM Transactions on Graphics · doi:10.1145/3355089.3356527 · Cited by 278 · see citing works

SpectralNet: Spectral Clustering using Deep Neural NetworksPreprint

Shaham, U., Stanton, K. P., Li, H., Nadler, B., Basri, R., & Kluger, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.01587 · Cited by 275 · see citing works

DiffusionNet: Discretization Agnostic Learning on Surfaces

Sharp, N., Attaiki, S., Crane, K., & Ovsjanikov, M. (2022) · ACM Transactions on Graphics · doi:10.1145/3507905 · Cited by 264 · see citing works

Interpolated Convolutional Networks for 3D Point Cloud Understanding

Mao, J., Wang, X., & Li, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00166 · Cited by 245 · see citing works

Multiresolution Tree Networks for 3D Point Cloud Processing

Gadelha, M., Wang, R., & Maji, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_7 · Cited by 241 · see citing works

Convolutional 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

HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-Scale Point Clouds

Gu, X., Wang, Y., Wu, C., Lee, Y. J., & Wang, P. (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.2019.00337 · Cited by 221 · see citing works

Fully-Convolutional Point Networks for Large-Scale Point Clouds

Rethage, D., Wald, J., Sturm, J., Navab, N., & Tombari, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_37 · Cited by 213 · see citing works

A survey of graph neural networks in various learning paradigms: methods, applications, and challenges

Waikhom, L., & Patgiri, R. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10321-2 · Cited by 203 · see citing works

Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds

Lei, H., Akhtar, N., & Mian, A. S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2983410 · Cited by 201 · see citing works

Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction

Qiu, H., Zheng, Q., Msahli, M., Memmi, G., Meikang Qiu, A., & Lu, J. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3032882 · Cited by 200 · see citing works

Unsupervised Multi-Task Feature Learning on Point Clouds

Hassani, K., & Haley, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00825 · Cited by 198 · see citing works

PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis

Cheng, S., Chen, X., He, X., Liu, Z., & Bai, X. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3072214 · Cited by 194 · see citing works

PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention

Sun, Y., Wang, Y., Liu, Z., Siegel, J. E., & Sarma, S. E. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093430 · Cited by 187 · see citing works

PointCNN: Convolution On $\mathcal{X}$-Transformed PointsPreprint

Li, Y., Bu, R., Sun, M., Wu, W., Di, X., & Chen, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.07791 · Cited by 187 · see citing works

FPConv: Learning Local Flattening for Point Convolution

Lin, Y., Yan, Z., Huang, H., Du, D., Liu, L., Cui, S. R., & Han, 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/cvpr42600.2020.00435 · Cited by 184 · see citing works

Self-Supervised Deep Learning on Point Clouds by Reconstructing SpacePreprint

Sauder, J. F., & Sievers, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.08396 · Cited by 182 · see citing works

Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers

Liu, D., Yu, R., & Su, H. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803770 · Cited by 179 · see citing works

A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective

Chen, C., Wu, Y., Dai, Q., Zhou, H.-Y., Xu, M., Yang, S., Han, X., & Yu, Y. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3445463 · Cited by 177 · see citing works

Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition

Rao, Y., Lu, J., & Zhou, 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.2019.00054 · Cited by 175 · see citing works

Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation

Bouritsas, G., Bokhnyak, S., Ploumpis, S., Zafeiriou, S. P., & Bronstein, M. M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00731 · Cited by 174 · see citing works

Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds

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Deep learning based point cloud registration: an overview

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