3D-MRL: Nested Multimodal 3D Representations via Matryoshka Representation Learning
Vision-Language Models align point clouds with image and text embeddings, enabling zero-shot recognition, retrieval, and open-vocabulary understanding of 3D shapes. Existing multimodal 3D pre-training methods produce fixed-dimensional embeddings, requiring separate models for different computational budgets. We propose 3D Matryoshka Representation Learning (3D-MRL), a multimodal 3D pre-training framework based on Matryoshka Representation Learning. 3D-MRL learns nested 3D representations by aligning point clouds with frozen CLIP image and text embeddings while applying contrastive supervision across multiple embedding dimensions. The Matryoshka objective is applied only to the 3D encoder,...
| Authors | Márcus Lobo, Vitor Matias, Jeová Farias et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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