根据点云模糊程度自适应调整对比学习边界，提升分割精度
Adaptive Margin Contrastive Learning for Ambiguity-aware 3D Semantic Segmentation
- 按点的模糊度动态调整对比学习的决策边界
- 在S3DIS和ScanNet上达到最优性能，优于现有方法
- 适合处理过渡区域模糊点，提升模型鲁棒性
本文提出一种面向3D点云语义分割的自适应边缘对比学习方法AMContrast3D。现有方法采用统一惩罚机制，忽略过渡区域中因模糊性导致的特征区分度低的问题。由于高度模糊的点对人类也难以区分，其标注可靠性较低，强约束会损害模型性能。为此，我们基于位置嵌入估计每个点的模糊度，并设计边缘生成器动态调整对比特征嵌入的决策边界：随着模糊度增加，边距缩小，极端模糊点甚至出现负边距。在大规模数据集S3DIS和ScanNet上的实验表明，该方法优于当前最优方法。
原文摘要 · Abstract (English)
In this paper, we propose an adaptive margin contrastive learning method for 3D point cloud semantic segmentation, namely AMContrast3D. Most existing methods use equally penalized objectives, which ignore per-point ambiguities and less discriminated features stemming from transition regions. However, as highly ambiguous points may be indistinguishable even for humans, their manually annotated labels are less reliable, and hard constraints over these points would lead to sub-optimal models. To address this, we design adaptive objectives for individual points based on their ambiguity levels, aiming to ensure the correctness of low-ambiguity points while allowing mistakes for high-ambiguity points. Specifically, we first estimate ambiguities based on position embeddings. Then, we develop a margin generator to shift decision boundaries for contrastive feature embeddings, so margins are narrowed due to increasing ambiguities with even negative margins for extremely high-ambiguity points. Experimental results on large-scale datasets, S3DIS and ScanNet, demonstrate that our method outperforms state-of-the-art methods.
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