HOLA: Holistic Multi-Modal Alignment for Open-Set 3D Recognition
For 3D recognition tasks requiring generalization to unseen categories, HOLA addresses the limitation of partial-view representations by holistic alignment, offering a practical solution for open-vocabulary 3D recognition.
HOLA proposes a multi-modal alignment method for open-set 3D recognition that aligns point clouds with multiple images and texts using a decoupled multi-positive contrastive loss, achieving state-of-the-art zero-shot performance on long-tail benchmarks while maintaining high frame rates.
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 competition. We introduce such a function, termed the decoupled multi-positive contrastive loss. Our formulation enhances the loss's hardness-aware focus on challenging negatives, avoiding the "spotlight crowding" that occurs when many positives share the same softmax with all the negatives. Complementing this, we present a lightweight text adapter applied only to web captions, reducing the domain gap to curated annotations and enabling effective use of large-scale unsupervised text. Our model demonstrates state-of-the-art open-vocabulary performance on long-tail benchmarks, yielding substantial zero-shot improvements while sustaining high frame rates.