通过频域全局模式分组超点，实现无监督3D语义分割新突破
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds
- 基于局部与全局特征联合建模，利用频域模式分组超点
- 在ScanNet、S3DIS和Semantic3D上显著超越现有方法
- 无需人工标注即可学习有意义的语义结构，适合无标签场景
我们研究了在未经标注的原始点云上进行无监督3D语义分割的问题。现有方法通常仅依赖点级局部特征并采用简单分组策略，难以发现超越局部特征的更丰富语义先验。本文提出LogoSP，从局部与全局点特征中共同学习3D语义。其核心思想是通过频域中的全局模式对超点进行分组，生成高精度的语义伪标签，用于训练分割网络。在两个室内和一个室外数据集上的大量实验表明，LogoSP显著优于所有现有无监督方法，达到当前最优性能。值得注意的是，对所学全局模式的分析显示，它们确实在无标注训练下捕捉到了有意义的3D语义。
原文摘要 · Abstract (English)
We study the problem of unsupervised 3D semantic segmentation on raw point clouds without needing human labels in training. Existing methods usually formulate this problem into learning per-point local features followed by a simple grouping strategy, lacking the ability to discover additional and possibly richer semantic priors beyond local features. In this paper, we introduce LogoSP to learn 3D semantics from both local and global point features. The key to our approach is to discover 3D semantic information by grouping superpoints according to their global patterns in the frequency domain, thus generating highly accurate semantic pseudo-labels for training a segmentation network. Extensive experiments on two indoor and an outdoor datasets show that our LogoSP surpasses all existing unsupervised methods by large margins, achieving the state-of-the-art performance for unsupervised 3D semantic segmentation. Notably, our investigation into the learned global patterns reveals that they truly represent meaningful 3D semantics in the absence of human labels during training.
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