RGB-Pointmap Pretraining for Unified 3D Scene Understanding
Pretraining 3D encoders through alignment with Contrastive Language-Image Pre-training (CLIP) has emerged as a promising direction for learning generalizable representations for 3D scene understanding. In this paper, we propose UniScene3D, a transformer-based framework that learns unified 3D scene representations from multi-view RGB-Pointmap inputs by leveraging the priors of a pretrained 2D foundation model. For robust RGB-Pointmap representation learning, we introduce cross-view geometric alignment and grounded view alignment to enforce geometric and semantic consistency across views. Extensive low-shot and task-specific fine-tuning on viewpoint grounding, scene retrieval, scene classif...
| Authors | Ye Mao, Weixun Luo, Ranran Huang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 2 July 2026 |
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