High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation

• URL: http://arxiv.org/abs/2506.23227v1
• Date: Sun, 29 Jun 2025 13:17:12 GMT
• Title: High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation
• Authors: Lunhao Duan, Shanshan Zhao, Xingxing Weng, Jing Zhang, Gui-Song Xia,
• Abstract summary: This paper investigates indoor point cloud semantic segmentation under scene-level annotation.<n>Current methods first generate point-level pseudo-labels, which are then used to train segmentation models.<n>To enhance accuracy, this paper proposes a high-quality pseudo-label generation framework.
• Score: 32.03087826213936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates indoor point cloud semantic segmentation under scene-level annotation, which is less explored compared to methods relying on sparse point-level labels. In the absence of precise point-level labels, current methods first generate point-level pseudo-labels, which are then used to train segmentation models. However, generating accurate pseudo-labels for each point solely based on scene-level annotations poses a considerable challenge, substantially affecting segmentation performance. Consequently, to enhance accuracy, this paper proposes a high-quality pseudo-label generation framework by exploring contemporary multi-modal information and region-point semantic consistency. Specifically, with a cross-modal feature guidance module, our method utilizes 2D-3D correspondences to align point cloud features with corresponding 2D image pixels, thereby assisting point cloud feature learning. To further alleviate the challenge presented by the scene-level annotation, we introduce a region-point semantic consistency module. It produces regional semantics through a region-voting strategy derived from point-level semantics, which are subsequently employed to guide the point-level semantic predictions. Leveraging the aforementioned modules, our method can rectify inaccurate point-level semantic predictions during training and obtain high-quality pseudo-labels. Significant improvements over previous works on ScanNet v2 and S3DIS datasets under scene-level annotation can demonstrate the effectiveness. Additionally, comprehensive ablation studies validate the contributions of our approach's individual components. The code is available at https://github.com/LHDuan/WSegPC .

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