Abstract
The lack of fine-grained 3D shape segmentation data is the main obstacle to developing learning-based 3D segmentation techniques. We propose an effective semi-supervised method for learning 3D segmentations from a few labeled 3D shapes and a large amount of unlabeled 3D data. For the unlabeled data, we present a novel multilevel consistency loss to enforce consistency of network predictions between perturbed copies of a 3D shape at multiple levels: point level, part level, and hierarchical level. For the labeled data, we develop a simple yet effective part substitution scheme to augment the labeled 3D shapes with more structural variations to enhance training. Our method has been extensively validated on the task of 3D object semantic segmentation on PartNet and ShapeNetPart, and indoor scene semantic segmentation on ScanNet. It exhibits superior performance to existing semi-supervised and unsupervised pre-training 3D approaches.
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Chun-Yu Sun proposed and implemented the key idea, conducted the main experiments, and contributed to paper writing. Yu-Qi Yang contributed to the comparison of unsupervised pretraining. Xin Tong and Hao-Xiang Guo contributed to the part substitution algorithm. Peng-Shuai Wang provided guidance on the design of the O-CNN backbone and data augmentation. Xin Tong and Heung-Yeung Shum supervised the findings of this work and verified the key idea. Yang Liu led the project and contributed to the key idea, experimental design, and paper writing.
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Chun-Yu Sun received his bachelor degree in computer science and technology from Xidian University in 2015. He is currently a Ph.D. student at the Institute for Advanced Study, Tsinghua University. His research interests include computer graphics and 3D Vision.
Yu-Qi Yang received his bachelor degree in computer science from the University of Science and Technology of China in 2018. He is currently a Ph.D. student at the Institute for Advanced Study, Tsinghua University. His research interests include computer graphics and 3D Vision.
Hao-Xiang Guo received his bachelor degree in applied mathematics from the University of Science and Technology of China in 2017. He is currently a Ph.D. student at the Institute for Advanced Study, Tsinghua University. His research interests include 3D reconstruction, geometry processing, and shape analysis.
Peng-Shuai Wang is a researcher at Microsoft Research Asia. He received his Ph.D. degree in computer science and bachelor degree in automation both from Tsinghua University in 2018 and 2013 respectively. His research interests include computer graphics and 3D vision.
Xin Tong is a principal research manager at Microsoft Research Asia, where he leads the Internet Graphics Group. He received his Ph.D. degree from Tsinghua University in 1999. His research interests include computer graphics and computer vision, including texture synthesis, appearance modeling, light transport simulation and acquisition, 3D facial animation, and data-driven geometric processing. He has been on the editorial boards of IEEE Transactions on Visualization and Computer graphics, ACM Transactions on Graphics, and Computer Graphics Forum.
Yang Liu is a principal researcher at Microsoft Research Asia. He received his Ph.D. degree from the University of Hong Kong in 2008, and master and bachelor degrees in computational mathematics from the University of Science and Technology of China in 2003 and 2000, respectively. His recent research focuses on geometry processing and 3D learning. He is on the editorial boards of IEEE Transactions on Visualization and Computer graphics and ACM Transactions on Graphics.
Heung-Yeung Shum is an adjunct professor at the Institute for Advanced Study, Tsinghua University. He received his Ph.D. degree in robotics from the School of Computer Science, Carnegie Mellon University. He was the Executive Vice President of Artificial Intelligence & Research at Microsoft until March 2020. His research spans computer vision, computer graphics, pattern recognition, statistical learning, and robotics. He is a Fellow of the Institute of Electrical and Electronics Engineers and a Fellow of the Association for Computing Machinery.
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Sun, CY., Yang, YQ., Guo, HX. et al. Semi-supervised 3D shape segmentation with multilevel consistency and part substitution. Comp. Visual Media 9, 229–247 (2023). https://doi.org/10.1007/s41095-022-0281-9
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DOI: https://doi.org/10.1007/s41095-022-0281-9