GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence

• URL: http://arxiv.org/abs/2606.08014v1
• Date: Sat, 06 Jun 2026 07:09:15 GMT
• Title: GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence
• Abstract summary: We propose a training-free 3D instance segmentation approach via Geometric Visual Correspondence (GVC-Seg)<n>GVC-Seg exploits the correspondence between 3D geometric cues and 2D visual cues to mitigate the confidence bias.<n>Our method achieves state-of-the-art performance on several challenging benchmarks, while also exhibiting strong potential in open-vocabulary semantic segmentation settings.
• Score: 50.10864740041483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate 3D instance segmentation in point cloud data is critical for machine vision applications. Recent advancements leverage multiple pre-trained foundation models to generate 3D proposals, followed by the application of proposal aggregation methods, which significantly enhance performance. However, they often produce sub-optimal results due to inherent variations in confidence levels across different segmentation models, resulting in a bias toward the model with higher confidence. This bias is inherently model-dependent and is influenced by factors such as data preprocessing techniques and training strategies. To address this bias, we propose a novel, training-free 3D instance segmentation approach via Geometric Visual Correspondence (GVC-Seg), which exploits the correspondence between 3D geometric cues and 2D visual cues to mitigate the confidence bias. Additionally, a 3D proposal generation module and a mask-aware CLIP feature extraction module are introduced during the instance mask generation and instance semantic reasoning, respectively. In this way, GVC-Seg enhances proposal quality assessment, ensuring unbiased ensemble learning across different models. Extensive experiments demonstrate that our method achieves state-of-the-art performance on several challenging benchmarks, while also exhibiting strong potential in open-vocabulary semantic segmentation settings.

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