PA-Net: Trustworthy weakly supervised point cloud semantic segmentation with primary–auxiliary structure
Weakly supervised point cloud segmentation is a hot research topic with significant implications for practical applications such as autonomous driving. However, existing weakly supervised approaches primarily focus on improving prediction results while neglecting the reliability of the inference out...
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Veröffentlicht in: | Computers & electrical engineering 2024-10, Vol.119, p.109555, Article 109555 |
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Sprache: | eng |
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Zusammenfassung: | Weakly supervised point cloud segmentation is a hot research topic with significant implications for practical applications such as autonomous driving. However, existing weakly supervised approaches primarily focus on improving prediction results while neglecting the reliability of the inference outcomes. To address this issue, this paper proposes a primary–auxiliary network PA-Net for point cloud segmentation and uncertainty estimation. PA-Net achieves excellent segmentation results and enables effective uncertainty assessment. Specifically, we first train PA-Net on sparsely annotated point cloud data, which allows our network to capture multi-scale information of labeled points and provide additional supervisory signals. Subsequently, we utilize the trained model for inference. We mainly conducted experiments on the indoor point cloud dataset S3DIS in a 1% labeled setting. PA-Net achieved a mean Intersection over Union (mIoU) of 64.5%, which surpasses most weakly-supervised methods. In particular, it exceeded PSD by 3.5% mIoU and SQN by 0.8% mIoU. Similarly, on the outdoor dataset Toronto3D, PA-Net also outperformed most weakly supervised methods. With only 0.01% labeling, it surpassed PSD by 11.99% mIoU and SQN by 20.34% mIoU. Lastly, to validate the effectiveness of uncertainty estimation, we visualized scene uncertainty, thereby demonstrating the practicality of our uncertainty estimation approach. Code will be made publicly available at https://github.com/NiuYingchun/PA-Net. |
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ISSN: | 0045-7906 |
DOI: | 10.1016/j.compeleceng.2024.109555 |