SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds

• URL: http://arxiv.org/abs/2104.04891v3
• Date: Thu, 27 Apr 2023 10:02:23 GMT
• Title: SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds
• Authors: Qingyong Hu, Bo Yang, Guangchi Fang, Yulan Guo, Ales Leonardis, Niki Trigoni, Andrew Markham
• Abstract summary: We propose a new weak supervision method to implicitly augment highly sparse supervision signals. The proposed Semantic Query Network (SQN) achieves promising performance on seven large-scale open datasets. SQN requires only 0.1% randomly annotated points for training, greatly reducing annotation cost and effort.
• Score: 69.97213386812969
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Labelling point clouds fully is highly time-consuming and costly. As larger point cloud datasets with billions of points become more common, we ask whether the full annotation is even necessary, demonstrating that existing baselines designed under a fully annotated assumption only degrade slightly even when faced with 1% random point annotations. However, beyond this point, e.g., at 0.1% annotations, segmentation accuracy is unacceptably low. We observe that, as point clouds are samples of the 3D world, the distribution of points in a local neighborhood is relatively homogeneous, exhibiting strong semantic similarity. Motivated by this, we propose a new weak supervision method to implicitly augment highly sparse supervision signals. Extensive experiments demonstrate the proposed Semantic Query Network (SQN) achieves promising performance on seven large-scale open datasets under weak supervision schemes, while requiring only 0.1% randomly annotated points for training, greatly reducing annotation cost and effort. The code is available at https://github.com/QingyongHu/SQN.

Related papers

• Multi-modality Affinity Inference for Weakly Supervised 3D Semantic Segmentation [47.81638388980828]
  We propose a simple yet effective scene-level weakly supervised point cloud segmentation method with a newly introduced multi-modality point affinity inference module. Our method outperforms the state-of-the-art by 4% to 6% mIoU on the ScanNet and S3DIS benchmarks.
  arXiv  Detail & Related papers  (2023-12-27T14:01:35Z)
• FreePoint: Unsupervised Point Cloud Instance Segmentation [72.64540130803687]
  We propose FreePoint, for underexplored unsupervised class-agnostic instance segmentation on point clouds. We represent point features by combining coordinates, colors, and self-supervised deep features. Based on the point features, we segment point clouds into coarse instance masks as pseudo labels, which are used to train a point cloud instance segmentation model.
  arXiv  Detail & Related papers  (2023-05-11T16:56:26Z)
• 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)
• Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation [78.6612285236938]
  We propose a novel DAT (textbfDual textbfAdaptive textbfTransformations) model for weakly supervised point cloud segmentation. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets.
  arXiv  Detail & Related papers  (2022-07-19T05:43:14Z)
• A new weakly supervised approach for ALS point cloud semantic segmentation [1.4620086904601473]
  We propose a deep-learning based weakly supervised framework for semantic segmentation of ALS point clouds. We exploit potential information from unlabeled data subject to incomplete and sparse labels. Our method achieves an overall accuracy of 83.0% and an average F1 score of 70.0%, which have increased by 6.9% and 12.8% respectively.
  arXiv  Detail & Related papers  (2021-10-04T14:00:23Z)
• 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)

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.