Abstract
3D weakly supervised semantic segmentation (3D WSSS) aims to achieve semantic segmentation by leveraging sparse or low-cost annotated data, significantly reducing reliance on dense point-wise annotations. Previous works mainly employ class activation maps or pre-trained vision-language models to address this challenge. However, the low quality of pseudo-labels and the insufficient exploitation of 3D geometric priors jointly create significant technical bottlenecks in developing high-performance 3D WSSS models. In this paper, we propose a simple yet effective 3D weakly supervised semantic segmentation method that integrates 3D geometric priors into a class-aware guidance mechanism to generate high-fidelity pseudo labels. Concretely, our designed methodology first employs Class-Aware Label Refinement module to generate more balanced and accurate pseudo labels for semantic categrories. This initial refinement stage focuses on enhancing label quality through category-specific optimization. Subsequently, the Geometry-Aware Label Refinement component is developed, which strategically integrates implicit 3D geometric constraints to effectively filter out low-confidence pseudo labels that fail to comply with geometric plausibility. Moreover, to address the challenge of extensive unlabeled regions, we propose a Label Update strategy that integrates Self-Training to propagate labels into these areas. This iterative process continuously enhances pseudo-label quality while expanding label coverage, ultimately fostering the development of high-performance 3D WSSS models. Comprehensive experimental validation reveals that our proposed methodology achieves state-of-the-art performance on both ScanNet and S3DIS benchmarks while demonstrating remarkable generalization capability in unsupervised settings, maintaining competitive accuracy through its robust design.
Abstract (translated)
3D弱监督语义分割（3D WSSS）的目标是通过利用稀疏或低成本的标注数据来实现语义分割，从而大大减少了对密集点注释的依赖。此前的工作主要采用类别激活图或预训练的视觉-语言模型来应对这一挑战。然而，伪标签的质量低和三维几何先验的利用不足共同构成了开发高性能3D WSSS模型的技术瓶颈。在本文中，我们提出了一种简单而有效的3D弱监督语义分割方法，该方法将3D几何先验整合到类感知引导机制中以生成高质量的伪标签。 具体而言，我们的设计方法首先采用类感知标签精炼模块来为语义类别生成更平衡且准确的伪标签。这一初步细化阶段侧重于通过特定类别的优化提升标签质量。随后开发了基于几何感知的标签精炼组件，该组件战略性地整合隐含的3D几何约束以有效过滤掉不符合几何合理性的低置信度伪标签。 此外，为了解决大量未标注区域的挑战，我们提出了一种结合自训练来将标签传播到这些区域的标签更新策略。这一迭代过程持续提升伪标签质量并扩大标签覆盖范围，最终促进高性能3D WSSS模型的发展。 全面的实验验证显示，我们的方法在ScanNet和S3DIS基准测试中均达到了最先进的性能，并展示了其在无监督环境中的卓越泛化能力，通过稳健的设计保持了竞争力的准确度。
URL
https://arxiv.org/abs/2510.17875
