Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud

• URL: http://arxiv.org/abs/2212.04744v1
• Date: Fri, 9 Dec 2022 09:42:26 GMT
• Title: Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud
• Authors: Yachao Zhang, Zonghao Li, Yuan Xie, Yanyun Qu, Cuihua Li, Tao Mei
• Abstract summary: 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.
• Score: 69.36717778451667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods\footnote{Code based on mindspore: https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}.

Related papers

• LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds [62.49198183539889]
  We propose a label-efficient semantic segmentation pipeline for outdoor scenes with LiDAR point clouds. Our method co-designs an efficient labeling process with semi/weakly supervised learning. Our proposed method is even highly competitive compared to the fully supervised counterpart with 100% labels.
  arXiv  Detail & Related papers  (2022-10-14T19:13:36Z)
• Unsupervised Representation Learning for 3D Point Cloud Data [66.92077180228634]
  We propose a simple yet effective approach for unsupervised point cloud learning. In particular, we identify a very useful transformation which generates a good contrastive version of an original point cloud. We conduct experiments on three downstream tasks which are 3D object classification, shape part segmentation and scene segmentation.
  arXiv  Detail & Related papers  (2021-10-13T10:52:45Z)
• 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)
• 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)
• Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds [67.0904905172941]
  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.
  arXiv  Detail & Related papers  (2020-03-29T14:13:29Z)

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.