3D Weakly Supervised Semantic Segmentation via Class-Aware and Geometry-Guided Pseudo-Label Refinement

• URL: http://arxiv.org/abs/2510.17875v1
• Date: Fri, 17 Oct 2025 03:53:43 GMT
• Title: 3D Weakly Supervised Semantic Segmentation via Class-Aware and Geometry-Guided Pseudo-Label Refinement
• Authors: Xiaoxu Xu, Xuexun Liu, Jinlong Li, Yitian Yuan, Qiudan Zhang, Lin Ma, Nicu Sebe, Xu Wang,
• Abstract summary: 3D weakly supervised semantic segmentation aims to achieve semantic segmentation by leveraging sparse or low-cost data.<n>Previous works mainly employ class activation maps or pre-trained vision-language models to address this challenge.<n>We propose a simple yet effective 3D weakly supervised semantic segmentation method that integrates 3D geometric priors into a class-aware guidance mechanism.
• Score: 49.05272731604324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning [0.0]
  3D object detection is essential for autonomous driving and robotic perception.<n>To reduce annotation dependency, unsupervised and sparsely-supervised paradigms have emerged.<n>This paper proposes SPL, a unified training framework for both Unsupervised and Sparsely-Supervised 3D Object Detection.
  arXiv  Detail & Related papers  (2026-02-25T01:26:34Z)
• UniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning [6.502142457981839]
  3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have advanced novel-view synthesis.<n>Recent methods extend multi-view 2D segmentation to 3D, enabling instance/semantic segmentation for better scene understanding.<n>Key challenge is the inconsistency of 2D instance labels across views, leading to poor 3D predictions.<n>We propose a unified framework that merges these steps, reducing training time and improving performance by introducing a learnable feature embedding for segmentation in Gaussian primitives.
  arXiv  Detail & Related papers  (2025-12-31T10:20:01Z)
• HQ-OV3D: A High Box Quality Open-World 3D Detection Framework based on Diffision Model [9.89023516462523]
  We propose a High Box Quality Open-Vocabulary 3D Detection (HQ-OV3D) framework, dedicated to generate and refine high-quality pseudo-labels.<n> HQ-OV3D can serve not only as a strong standalone open-vocabulary 3D detector but also as a plug-in high-quality pseudo-label generator for existing openvocabulary detection or annotation pipelines.
  arXiv  Detail & Related papers  (2025-08-12T18:57:18Z)
• 3DResT: A Strong Baseline for Semi-Supervised 3D Referring Expression Segmentation [73.877177695218]
  3D Referring Expression (3D-RES) typically requires extensive instance-level annotations, which are time-consuming and costly.<n>Semi-supervised learning (SSL) mitigates this by using limited labeled data alongside abundant unlabeled data, improving performance while reducing annotation costs.<n>In this paper, we introduce the first semi-supervised learning framework for 3D-RES, presenting a robust baseline method named 3DResT.
  arXiv  Detail & Related papers  (2025-04-17T02:50:52Z)
• SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts [13.349110509879312]
  sparsely-supervised 3D object detection has gained great attention, achieving performance close to fully-supervised 3D objectors.<n>We propose a boosting strategy, termed SP3D, to boost the 3D detector with robust feature discrimination capability under sparse annotation settings.<n> Experiments have validated that SP3D can enhance the performance of sparsely supervised detectors by a large margin under meager labeling conditions.
  arXiv  Detail & Related papers  (2025-03-09T06:08:04Z)
• Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection [108.672972439282]
  We introduce a novel decoupled pseudo-labeling (DPL) approach for SSM3OD. Our approach features a Decoupled Pseudo-label Generation (DPG) module, designed to efficiently generate pseudo-labels. We also present a DepthGradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels.
  arXiv  Detail & Related papers  (2024-03-26T05:12:18Z)
• Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning [59.64695628433855]
  We propose a novel cross-modality weakly supervised method for 3D segmentation, incorporating complementary information from unlabeled images. Basically, we design a dual-branch network equipped with an active labeling strategy, to maximize the power of tiny parts of labels. Our method even outperforms the state-of-the-art fully supervised competitors with less than 1% actively selected annotations.
  arXiv  Detail & Related papers  (2022-09-16T07:59:04Z)
• Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training [57.25828870799331]
  We propose STMono3D, a new self-teaching framework for unsupervised domain adaptation on Mono3D. We develop a teacher-student paradigm to generate adaptive pseudo labels on the target domain. STMono3D achieves remarkable performance on all evaluated datasets and even surpasses fully supervised results on the KITTI 3D object detection dataset.
  arXiv  Detail & Related papers  (2022-04-25T12:23:07Z)
• 3D Spatial Recognition without Spatially Labeled 3D [127.6254240158249]
  We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition. We show that WyPR can detect and segment objects in point cloud data without access to any spatial labels at training time.
  arXiv  Detail & Related papers  (2021-05-13T17:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.