All in One: Visual-Description-Guided Unified Point Cloud Segmentation

• URL: http://arxiv.org/abs/2507.05211v2
• Date: Fri, 25 Jul 2025 14:03:22 GMT
• Title: All in One: Visual-Description-Guided Unified Point Cloud Segmentation
• Authors: Zongyan Han, Mohamed El Amine Boudjoghra, Jiahua Dong, Jinhong Wang, Rao Muhammad Anwer,
• Abstract summary: VDG-Uni3DSeg is a novel framework that integrates pre-trained vision-language models and large language models.<n>Our method incorporates rich multimodal cues, facilitating fine-grained class and instance separation.
• Score: 26.46051445945897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unified segmentation of 3D point clouds is crucial for scene understanding, but is hindered by its sparse structure, limited annotations, and the challenge of distinguishing fine-grained object classes in complex environments. Existing methods often struggle to capture rich semantic and contextual information due to limited supervision and a lack of diverse multimodal cues, leading to suboptimal differentiation of classes and instances. To address these challenges, we propose VDG-Uni3DSeg, a novel framework that integrates pre-trained vision-language models (e.g., CLIP) and large language models (LLMs) to enhance 3D segmentation. By leveraging LLM-generated textual descriptions and reference images from the internet, our method incorporates rich multimodal cues, facilitating fine-grained class and instance separation. We further design a Semantic-Visual Contrastive Loss to align point features with multimodal queries and a Spatial Enhanced Module to model scene-wide relationships efficiently. Operating within a closed-set paradigm that utilizes multimodal knowledge generated offline, VDG-Uni3DSeg achieves state-of-the-art results in semantic, instance, and panoptic segmentation, offering a scalable and practical solution for 3D understanding. Our code is available at https://github.com/Hanzy1996/VDG-Uni3DSeg.

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