Hierarchical Image-Guided 3D Point Cloud Segmentation in Industrial Scenes via Multi-View Bayesian Fusion

• URL: http://arxiv.org/abs/2512.06882v1
• Date: Sun, 07 Dec 2025 15:15:52 GMT
• Title: Hierarchical Image-Guided 3D Point Cloud Segmentation in Industrial Scenes via Multi-View Bayesian Fusion
• Authors: Yu Zhu, Naoya Chiba, Koichi Hashimoto,
• Abstract summary: 3D segmentation is critical for understanding complex scenes with dense layouts and multi-scale objects.<n>Existing 3D point-based methods require costly annotations, while image-guided methods often suffer from semantic inconsistencies across views.<n>We propose a hierarchical image-guided 3D segmentation framework that progressively refines segmentation from instance-level to part-level.
• Score: 4.679314646805623
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reliable 3D segmentation is critical for understanding complex scenes with dense layouts and multi-scale objects, as commonly seen in industrial environments. In such scenarios, heavy occlusion weakens geometric boundaries between objects, and large differences in object scale will cause end-to-end models fail to capture both coarse and fine details accurately. Existing 3D point-based methods require costly annotations, while image-guided methods often suffer from semantic inconsistencies across views. To address these challenges, we propose a hierarchical image-guided 3D segmentation framework that progressively refines segmentation from instance-level to part-level. Instance segmentation involves rendering a top-view image and projecting SAM-generated masks prompted by YOLO-World back onto the 3D point cloud. Part-level segmentation is subsequently performed by rendering multi-view images of each instance obtained from the previous stage and applying the same 2D segmentation and back-projection process at each view, followed by Bayesian updating fusion to ensure semantic consistency across views. Experiments on real-world factory data demonstrate that our method effectively handles occlusion and structural complexity, achieving consistently high per-class mIoU scores. Additional evaluations on public dataset confirm the generalization ability of our framework, highlighting its robustness, annotation efficiency, and adaptability to diverse 3D environments.

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