UniPLV: Towards Label-Efficient Open-World 3D Scene Understanding by Regional Visual Language Supervision

• URL: http://arxiv.org/abs/2412.18131v2
• Date: Wed, 17 Sep 2025 14:37:32 GMT
• Title: UniPLV: Towards Label-Efficient Open-World 3D Scene Understanding by Regional Visual Language Supervision
• Authors: Yuru Wang, Pei Liu, Songtao Wang, Zehan Zhang, Xinyan Lu, Changwei Cai, Hao Li, Fu Liu, Peng Jia, Xianpeng Lang,
• Abstract summary: We present UniPLV, a robust framework that unifies point clouds, images, and text within a single learning paradigm for comprehensive 3D scene understanding.<n>We show that UniPLV significantly surpasses state-of-the-art methods, with average improvements of 15.6% and 14.8% in semantic segmentation for Base-Annotated and.<n>Free tasks.
• Score: 10.587237925455211
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-world 3D scene understanding is a critical challenge that involves recognizing and distinguishing diverse objects and categories from 3D data, such as point clouds, without relying on manual annotations. Traditional methods struggle with this open-world task, especially due to the limitations of constructing extensive point cloud-text pairs and handling multimodal data effectively. In response to these challenges, we present UniPLV, a robust framework that unifies point clouds, images, and text within a single learning paradigm for comprehensive 3D scene understanding. UniPLV leverages images as a bridge to co-embed 3D points with pre-aligned images and text in a shared feature space, eliminating the need for labor-intensive point cloud-text pair crafting. Our framework achieves precise multimodal alignment through two innovative strategies: (i) Logit and feature distillation modules between images and point clouds to enhance feature coherence; (ii) A vision-point matching module that implicitly corrects 3D semantic predictions affected by projection inaccuracies from points to pixels. To further boost performance, we implement four task-specific losses alongside a two-stage training strategy. Extensive experiments demonstrate that UniPLV significantly surpasses state-of-the-art methods, with average improvements of 15.6% and 14.8% in semantic segmentation for Base-Annotated and Annotation-Free tasks, respectively. These results underscore UniPLV's efficacy in pushing the boundaries of open-world 3D scene understanding. We will release the code to support future research and development.

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