Shape Self-Correction for Unsupervised Point Cloud Understanding
We develop a novel self-supervised learning method named Shape Self-Correction for point cloud analysis. Our method is motivated by the principle that a good shape representation should be able to find distorted parts of a shape and correct them. To learn strong shape representations in an unsupervised manner, we first design a shape-disorganizing module to destroy certain local shape parts of an object. Then the destroyed shape and the normal shape are sent into a point cloud network to get representations, which are employed to segment points that belong to distorted parts and further reconstruct them to restore the shape to normal. To perform better in these two associated pretext tasks, the network is constrained to capture useful shape features from the object, which indicates that the point cloud network encodes rich geometric and contextual information. The learned feature extractor transfers well to downstream classification and segmentation tasks. Experimental results on ModelNet, ScanNet and ShapeNetPart demonstrate that our method achieves state-of-the-art performance among unsupervised methods. Our framework can be applied to a wide range of deep learning networks for point cloud analysis and we show experimentally that pre-training with our framework significantly boosts the performance of supervised models.
Code (0)
등록된 구현이 없습니다.
Tasks
Self-Supervised LearningSimilar Papers 제목 키워드 기반
SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence
Unsupervised point cloud shape correspondence aims to obtain dense point-to-point correspondences between point clouds without manually annotated pairs. However, humans and some animals have bilateral symmetry and variou…
3D Dense Shape CorrespondenceUnsupervised Multi-Task Feature Learning on Point Clouds
We introduce an unsupervised multi-task model to jointly learn point and shape features on point clouds. We define three unsupervised tasks including clustering, reconstruction, and self-supervised classification to trai…
ClassificationClusteringGeneral ClassificationSegmentationRefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation
Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations. Reliance on multi-task learning to align features across domains has been the stand…
Domain AdaptationMulti-Task LearningPoint Cloud ClassificationUnsupervised Domain AdaptationEditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation
This paper tackles the problem of parts-aware point cloud generation. Unlike existing works which require the point cloud to be segmented into parts a priori, our parts-aware editing and generation are performed in an un…
Inductive BiasPoint Cloud GenerationUnsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds
Representing complex 3D objects as simple geometric primitives, known as shape abstraction, is important for geometric modeling, structural analysis, and shape synthesis. In this paper, we propose an unsupervised shape a…
ClusteringSegmentationSelf-Learning