Weakly Supervised LiDAR Semantic Segmentation via Scatter Image Annotation

• URL: http://arxiv.org/abs/2404.12861v2
• Date: Mon, 12 Aug 2024 09:53:29 GMT
• Title: Weakly Supervised LiDAR Semantic Segmentation via Scatter Image Annotation
• Authors: Yilong Chen, Zongyi Xu, xiaoshui Huang, Ruicheng Zhang, Xinqi Jiang, Xinbo Gao,
• Abstract summary: We implement LiDAR semantic segmentation using scatter image annotation. We also propose ScatterNet, a network that includes three pivotal strategies to reduce the performance gap. Our method requires less than 0.02% of the labeled points to achieve over 95% of the performance of fully-supervised methods.
• Score: 38.715754110667916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised LiDAR semantic segmentation has made significant strides with limited labeled data. However, most existing methods focus on the network training under weak supervision, while efficient annotation strategies remain largely unexplored. To tackle this gap, we implement LiDAR semantic segmentation using scatter image annotation, effectively integrating an efficient annotation strategy with network training. Specifically, we propose employing scatter images to annotate LiDAR point clouds, combining a pre-trained optical flow estimation network with a foundation image segmentation model to rapidly propagate manual annotations into dense labels for both images and point clouds. Moreover, we propose ScatterNet, a network that includes three pivotal strategies to reduce the performance gap caused by such annotations. Firstly, it utilizes dense semantic labels as supervision for the image branch, alleviating the modality imbalance between point clouds and images. Secondly, an intermediate fusion branch is proposed to obtain multimodal texture and structural features. Lastly, a perception consistency loss is introduced to determine which information needs to be fused and which needs to be discarded during the fusion process. Extensive experiments on the nuScenes and SemanticKITTI datasets have demonstrated that our method requires less than 0.02% of the labeled points to achieve over 95% of the performance of fully-supervised methods. Notably, our labeled points are only 5% of those used in the most advanced weakly supervised methods.

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