Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds

• URL: http://arxiv.org/abs/2003.13035v1
• Date: Sun, 29 Mar 2020 14:13:29 GMT
• Title: Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds
• Authors: Jiacheng Wei, Guosheng Lin, Kim-Hui Yap, Tzu-Yi Hung, Lihua Xie
• Abstract summary: We propose a weakly supervised approach to predict point-level results using weak labels on 3D point clouds. To the best of our knowledge, this is the first method that uses cloud-level weak labels on raw 3D space to train a point cloud semantic segmentation network.
• Score: 67.0904905172941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point clouds provide intrinsic geometric information and surface context for scene understanding. Existing methods for point cloud segmentation require a large amount of fully labeled data. Using advanced depth sensors, collection of large scale 3D dataset is no longer a cumbersome process. However, manually producing point-level label on the large scale dataset is time and labor-intensive. In this paper, we propose a weakly supervised approach to predict point-level results using weak labels on 3D point clouds. We introduce our multi-path region mining module to generate pseudo point-level label from a classification network trained with weak labels. It mines the localization cues for each class from various aspects of the network feature using different attention modules. Then, we use the point-level pseudo labels to train a point cloud segmentation network in a fully supervised manner. To the best of our knowledge, this is the first method that uses cloud-level weak labels on raw 3D space to train a point cloud semantic segmentation network. In our setting, the 3D weak labels only indicate the classes that appeared in our input sample. We discuss both scene- and subcloud-level weakly labels on raw 3D point cloud data and perform in-depth experiments on them. On ScanNet dataset, our result trained with subcloud-level labels is compatible with some fully supervised methods.

Related papers

• Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud [69.36717778451667]
  Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. We propose an effective weakly supervised method containing two components to solve the problem. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods.
  arXiv  Detail & Related papers  (2022-12-09T09:42:26Z)
• Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision [65.19589997822155]
  We introduce a neural architecture, termed Box2Seg, to learn point-level semantics of 3D point clouds with bounding box-level supervision. We show that the proposed network can be trained with cheap, or even off-the-shelf bounding box-level annotations and subcloud-level tags.
  arXiv  Detail & Related papers  (2022-01-09T09:07:48Z)
• Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds [59.63231842439687]
  We train a semantic point cloud segmentation network with only a small portion of points being labeled. We propose a cross-sample feature reallocating module to transfer similar features and therefore re-route the gradients across two samples. Our weakly supervised method with only 10% and 1% of labels can produce compatible results with the fully supervised counterpart.
  arXiv  Detail & Related papers  (2021-07-23T14:34:57Z)
• SCSS-Net: Superpoint Constrained Semi-supervised Segmentation Network for 3D Indoor Scenes [6.3364439467281315]
  We propose a superpoint constrained semi-supervised segmentation network for 3D point clouds, named as SCSS-Net. Specifically, we use the pseudo labels predicted from unlabeled point clouds for self-training, and the superpoints produced by geometry-based and color-based Region Growing algorithms are combined to modify and delete pseudo labels with low confidence.
  arXiv  Detail & Related papers  (2021-07-08T04:43:21Z)
• SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network [21.818744369503197]
  We propose a semi-supervised semantic point cloud segmentation network, named SSPC-Net. We train the semantic segmentation network by inferring the labels of unlabeled points from the few annotated 3D points. Our method can achieve better performance than the current semi-supervised segmentation method with fewer annotated 3D points.
  arXiv  Detail & Related papers  (2021-04-16T02:37:27Z)
• Few-shot 3D Point Cloud Semantic Segmentation [138.80825169240302]
  We propose a novel attention-aware multi-prototype transductive few-shot point cloud semantic segmentation method. Our proposed method shows significant and consistent improvements compared to baselines in different few-shot point cloud semantic segmentation settings.
  arXiv  Detail & Related papers  (2020-06-22T08:05:25Z)
• Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels [77.65554439859967]
  We propose a weakly supervised point cloud segmentation approach which requires only a tiny fraction of points to be labelled in the training stage. Experiments are done on three public datasets with different degrees of weak supervision.
  arXiv  Detail & Related papers  (2020-04-08T16:14:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.