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3D Graph Neural Networks for RGBD Semantic Segmentation
Abstract
RGBD semantic segmentation requires joint reasoning about 2D appearance and 3D geometric information. In this paper we propose a 3D graph neural network (3DGNN) that builds a k-nearest neighbor graph on top of 3D point cloud. Each node in the graph corresponds to a set of points and is associated with a hidden representation vector initialized with an appearance feature extracted by a unary CNN from 2D images. Relying on recurrent functions, every node dynamically updates its hidden representation based on the current status and incoming messages from its neighbors. This propagation model is unrolled for a certain number of time steps and the final per-node representation is used for predicting the semantic class of each pixel. We use back-propagation through time to train the model. Extensive experiments on NYUD2 and SUN-RGBD datasets demonstrate the effectiveness of our approach.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
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
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438
Self-citationSpatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971
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
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
Research works printed but not linked: 2
- defferrard, 2016, Convolutional neural networks on graphs with fast localized spectral filtering, NIPS
- lin, 2016, Refinenet Multi-path refinement networks with identity mappings for high-resolution semantic segmentation
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Tan, J., Wang, K., Chen, L., Zhang, G., Li, J., & Zhang, X. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104129 · Cited by 8
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Cai, S., Li, L., Han, X., Luo, J., Zha, Z.-J., & Huang, Q. (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/cvpr52688.2022.01059 · Cited by 8
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Peng, S., & Mine, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.14734 · Cited by 8
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Li, Z., & Liao, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126217 · Cited by 7
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Yoo, H. (2020) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3386263.3409103 · Cited by 7
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He, Y. M., Tian, L., Zhang, L., & Zeng, X. R. (2021) · Complexity · doi:10.1155/2021/8041029 · Cited by 7
Dhingra, N., Chogovadze, G., & Kunz, A. M. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00102 · Cited by 7
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Wang, R., Wang, L., Dong, P., Jiao, L., & Chen, J. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9555003 · Cited by 7
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Liu, X., Su, Y., & Xu, B. (2021) · International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) · doi:10.1109/mlbdbi54094.2021.00140 · Cited by 7
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Su, Z., Zhou, G., Luo, F., Li, S., & Ma, K. (2022) · Remote Sensing · doi:10.3390/rs14225649 · Cited by 7
Wang, Q., & Breckon, T. P. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00020 · Cited by 7
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Pham, Q.-H., Hua, B., Nguyen, D. T., & Yeung, S.-K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.00257 · Cited by 7
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Dokania, S., & Singh, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.11117 · Cited by 7
Dou, Z., Cui, H., Zhang, L., & Wang, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.01284 · Cited by 8
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Swaroop Bhandary, K., Hochgeschwender, N., Plöger, P. G., Kirchner, F., & Valdenegro-Toro, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.01787 · Cited by 7
Garg, R., Qin, E., Martinez, F., Guirado, R., Jain, A., Abadal, S., Abellán, J. L., Acacio, M. E., Alarcón, E., Rajamanickam, S., et al. (2021) · doi:10.2172/1821960 · Cited by 7
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Haber, G. E., Viquerat, J., Larcher, A., Alves, J., Costes, F., Perchat, E., & Hachem, E. (2023) · Physics of Fluids · doi:10.1063/5.0134421 · Cited by 6
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Pan, W., Xia, Z., Ma, W., Wang, Y., Gu, L., Shi, G., & Zhao, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112545 · Cited by 6
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Ghalamsiah, N., Wen, J., Wu, T., Candan, K. S., & O’Neill, Z. D. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.114055 · Cited by 6
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Li, L., Zhao, S., Sun, R., Chai, X., Zheng, S., Chen, X., & Lv, Z. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/8861446 · Cited by 6
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Li, L., Zheng, W., Kong, L., Özgüner, Ü., Hou, W., & Lian, J. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500467 · Cited by 6
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Liu, W., & Wu, J. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00630-5 · Cited by 5
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Xu, C., Chen, F., Jia, Q., Wang, Y., Jin, L., Li, Y., Zhao, Y., & Zhao, C. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3543054 · Cited by 4
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Chen, H., Sun, F., Yuan, J., & Huang, Y. (2020) · Information Sciences · doi:10.1016/j.ins.2020.11.006 · Cited by 4
Kourbane, I., & Genç, Y. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116564 · Cited by 4
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Wang, C., Peipei, Z., Zhang, Y., Sun, J., Lu, B., Xu, Z., Su, B., & Li, X. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3392448 · Cited by 4
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Song, Y., Sun, Z., Wu, Y., & Li, H. (2019) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2019.101775 · Cited by 4
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Cong, R., Chen, H., Zhu, H., & Fu, H. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-28603-3_10 · Cited by 4
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Zhang, Z. (2021) · doi:10.3990/1.9789036552653 · Cited by 4
Zhao, J., & Chen, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15344-7 · Cited by 3
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Gao, R., & Qi, Y. (2025) · Sensors · doi:10.3390/s25072110 · Cited by 3
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Jiang, X., Wang, B., Wan, X., Chen, S., Fujita, H., & Aljuaid, H. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122303 · Cited by 3
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Pacot, M. P. B., & Marcos, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47665-5_24 · Cited by 3
Zhong, L., Lin, R., Jiayin Li, A., Wang, S., & Chen, Z. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671870 · Cited by 3
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Shi, W., Zhu, D., Zhang, G., Chen, L., Wang, L., Li, J., & Zhang, X. (2019) · IEEE International Conference on Cyborg and Bionic Systems (CBS) · doi:10.1109/cbs46900.2019.9114405 · Cited by 3
Yi, X., Zhang, H., Wang, Y., Guo, S., Wu, J., & Fan, C. (2022) · Sensors · doi:10.3390/s22166229 · Cited by 3
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Lian, Y., Shi, Y., Liu, Z., Jiang, B., & Li, X. (2024) · IET Image Processing · doi:10.1049/ipr2.13142 · Cited by 3
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Kim, S., Kim, S., Lee, J., & Yoo, H. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9181100 · Cited by 3
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Han, L., Piao, J., Lan, P., Wang, X., Yu, B., Tong, Y., & Xu, S. (2021) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2021.18280 · Cited by 3
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Gu, Z., Niu, L., & Zhang, L. (2019) · arXiv (Cornell University) · Cited by 3
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Mahdavi, M., Fooladgar, F., & Shohreh Kasaei, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12082 · Cited by 3
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Felicioni, S., & Dimiccoli, M. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506690 · Cited by 2
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Yang, L., Jiang, Z.-X., Zhou, H., & Guo, J. (2021) · International Journal of Embedded Systems · doi:10.1504/ijes.2021.116135 · Cited by 2
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Xia, Y., Gu, C., & Wu, K. (2022) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc55256.2022.10033547 · Cited by 2
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Jia, D., Gao, H., Su, X., Wu, F., & Zhao, J. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8076-5_32 · Cited by 3
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Parikh, D., Zhang, B., Kannan, R., Prasanna, V. K., & Busart, C. (2023) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec58863.2023.10363595 · Cited by 2
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Ge, B., Zhu, X., Tang, Z., Xia, C., Lu, Y., & Chen, Z. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01459-w · Cited by 2
Hardjianto, M., Istiyanto, J. E., Tjoa, A. M., Syahrulfath, A. S., Purnama, S. R., Sari, R. H., Hakim, Z., Fuadin, M. R., & Ananto, N. (2025) · ETRI Journal · doi:10.4218/etrij.2023-0190 · Cited by 2
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Wang, J., Chiu, K. N., & Fuge, M. (2020) · ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE) · doi:10.1115/detc2020-22714 · Cited by 2
Metadata
- Authors
- Qi, X., Liao, R., Jia, J., Fidler, S., & Urtasun, R.
- Affiliations
- Chinese University of Hong Kong (HK)
- University of Toronto (CA)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5209-5218
- OpenAlex
- W2776622059
- Cited by
- 482
- References
- 49
- Citations by year
- 2026: 9 · 2025: 28 · 2024: 39 · 2023: 52 · 2022: 62 · 2021: 122 · 2020: 71 · 2019: 68 · 2018: 30
- FWCI
- 70.80
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- 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 › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- point clouds, k-nearest neighbor graph, backpropagation through time, appearance features
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