Contrastive Language-Colored Pointmap Pretraining for Unified 3D Scene Understanding

• URL: http://arxiv.org/abs/2604.02546v1
• Date: Thu, 02 Apr 2026 21:54:43 GMT
• Title: Contrastive Language-Colored Pointmap Pretraining for Unified 3D Scene Understanding
• Abstract summary: We propose UniScene3D, a transformer-based encoder that learns unified scene representations from multi-view colored pointmaps.<n>For robust colored pointmap representation learning, we introduce novel cross-view geometric alignment and grounded view alignment.<n>These results highlight the effectiveness of our approach for unified 3D scene understanding.
• Score: 20.492325896478555
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Pretraining 3D encoders by aligning with Contrastive Language Image Pretraining (CLIP) has emerged as a promising direction to learn generalizable representations for 3D scene understanding. In this paper, we propose UniScene3D, a transformer-based encoder that learns unified scene representations from multi-view colored pointmaps, jointly modeling image appearance and geometry. For robust colored pointmap representation learning, we introduce novel cross-view geometric alignment and grounded view alignment to enforce cross-view geometry and semantic consistency. Extensive low-shot and task-specific fine-tuning evaluations on viewpoint grounding, scene retrieval, scene type classification, and 3D VQA demonstrate our state-of-the-art performance. These results highlight the effectiveness of our approach for unified 3D scene understanding. https://yebulabula.github.io/UniScene3D/

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