[2604.02546] Contrastive Language-Colored Pointmap Pretraining for Unified 3D Scene Understanding

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.02546 (cs) [Submitted on 2 Apr 2026] Title:Contrastive Language-Colored Pointmap Pretraining for Unified 3D Scene Understanding Authors:Ye Mao, Weixun Luo, Ranran Huang, Junpeng Jing, Krystian Mikolajczyk View a PDF of the paper titled Contrastive Language-Colored Pointmap Pretraining for Unified 3D Scene Understanding, by Ye Mao and 4 other authors View PDF HTML (experimental) 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. this https URL Comments: Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2604.02546 [cs.CV]   (or arXiv:2604.02546v1 [cs.CV] for this ve...