Unsupervised 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 train a multi-scale graph-based encoder. We evaluate our model on shape classification and segmentation benchmarks. The results suggest that it outperforms prior state-of-the-art unsupervised models: In the ModelNet40 classification task, it achieves an accuracy of 89.1% and in ShapeNet segmentation task, it achieves an mIoU of 68.2 and accuracy of 88.6%.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationClusteringGeneral ClassificationSegmentationSimilar Papers 제목 키워드 기반
FreePoint: Unsupervised Point Cloud Instance Segmentation
Instance segmentation of point clouds is a crucial task in 3D field with numerous applications that involve localizing and segmenting objects in a scene. However, achieving satisfactory results requires a large number of…
Instance SegmentationSegmentationSemantic SegmentationUnsupervised Pre-trainingPointDC:Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering
Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to the challenging topic of learning from unlabeled or weaker forms of annotations. In this…
ClusteringSegmentationSemantic SegmentationUnsupervised Semantic SegmentationPointDC: Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-Modal Distillation and Super-Voxel Clustering
Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to a challenging topic of learning from unlabeled or weaker form of annotations. In thi…
ClusteringSegmentationSemantic SegmentationUnsupervised Semantic SegmentationUnsupervised Learning of Global Registration of Temporal Sequence of Point Clouds
Global registration of point clouds aims to find an optimal alignment of a sequence of 2D or 3D point sets. In this paper, we present a novel method that takes advantage of current deep learning techniques for unsupervis…
UnPWC-SVDLO: Multi-SVD on PointPWC for Unsupervised Lidar Odometry
High-precision lidar odomety is an essential part of autonomous driving. In recent years, deep learning methods have been widely used in lidar odomety tasks, but most of the current methods only extract the global featur…
Autonomous DrivingPose EstimationScene Flow Estimation