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The lack of fine-grained 3D shape seg-mentation data is the main obstacle to developing learning-based 3D segmentation techniques. We pro-pose 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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Semi-supervised 3D shape segmentation with multilevel consistency and part substitution

Show Author's information Chun-Yu Sun1Yu-Qi Yang1Hao-Xiang Guo1Peng-Shuai Wang2Xin Tong2Yang Liu2( )Heung-Yeung Shum1
Institute for Advanced Study, Tsinghua University, Beijing 100084, China
Microsoft Research Asia, Beijing 100080, China

Abstract

The lack of fine-grained 3D shape seg-mentation data is the main obstacle to developing learning-based 3D segmentation techniques. We pro-pose 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.

Keywords:

shape segmentation, semi-supervised lear-ning, multilevel consistency
Received: 06 January 2022 Accepted: 05 March 2022 Published: 03 January 2023 Issue date: June 2023
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Received: 06 January 2022
Accepted: 05 March 2022
Published: 03 January 2023
Issue date: June 2023

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