Abstract
Recent advances in self-supervised learning (SSL) have shown tremendous potential for learning 3D point cloud representations without human annotations. However, SSL for 3D point clouds still faces critical challenges due to irregular geometry, shortcut-prone reconstruction, and unbalanced semantics distribution. In this work, we propose DOS (Distilling Observable Softmaps), a novel SSL framework that self-distills semantic relevance softmaps only at observable (unmasked) points. This strategy prevents information leakage from masked regions and provides richer supervision than discrete token-to-prototype assignments. To address the challenge of unbalanced semantics in an unsupervised setting, we introduce Zipfian prototypes and incorporate them using a modified Sinkhorn-Knopp algorithm, Zipf-Sinkhorn, which enforces a power-law prior over prototype usage and modulates the sharpness of the target softmap during training. DOS outperforms current state-of-the-art methods on semantic segmentation and 3D object detection across multiple benchmarks, including nuScenes, Waymo, SemanticKITTI, ScanNet, and ScanNet200, without relying on extra data or annotations. Our results demonstrate that observable-point softmaps distillation offers a scalable and effective paradigm for learning robust 3D representations.
Abstract (translated)
最近的自监督学习（SSL）进展展示了在没有人类注释的情况下学习三维点云表示的巨大潜力。然而，由于不规则几何形状、容易出现捷径问题的重建和不平衡的语义分布，对于三维点云的SSL仍然面临严峻挑战。为此，我们提出了DOS（Distilling Observable Softmaps），这是一种新颖的SSL框架，仅在可观察（未遮罩）点上自我提炼语义相关性软映射。这种策略可以防止信息从被掩码区域泄漏，并且相比离散标记到原型分配提供了更丰富的监督信号。为了应对无监督设置中不平衡语义的问题，我们引入了齐夫定律原型并通过修改后的Sinkhorn-Knopp算法（Zipf-Sinkhorn）将其整合，该算法在原型使用上施加幂律先验并在训练过程中调节目标软映射的锐度。 DOS在nuScenes、Waymo、SemanticKITTI、ScanNet和ScanNet200等多个基准测试中，在语义分割和三维物体检测任务上超越了当前最先进的方法，且无需依赖额外的数据或注释。我们的结果表明，可观察点软映射提炼提供了一种可扩展且有效的学习稳健三维表示的范式。
URL
https://arxiv.org/abs/2512.11465
