HOLA: Holistic Multi-Modal Alignment for Open-Set 3D Recognition
Open-set 3D recognition requires models that generalize to rare or unseen categories. Recent approaches address this by distilling language-vision knowledge into 3D encoders, typically relying on heavy 2D ViTs and aligning each point cloud with a single image or caption, thus anchoring representations to partial views. We propose aligning each point cloud with multiple images and textual descriptions to capture a more holistic understanding of 3D objects. To realize this idea, it is essential to design a loss function capable of jointly aligning a 3D instance with multiple matched signals, multi-view images and multiple texts, while separating positive aggregation from negative competitio...
| Authors | Koby Aharonov, Oren Shrout, Ayellet Tal |
|---|---|
| Primary category | cs.CV |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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