Point Cloud Quantization through Multimodal Prompting for 3D Understanding

TL;DR

This paper introduces a multimodal prompting-based point cloud quantization method that leverages text embeddings as prototypes and uses regularization and Gumbel-Softmax for effective 3D data representation.

Contribution

It proposes a novel multimodal prompting-driven quantization framework that improves prototype robustness and interpretability for 3D point cloud analysis.

Findings

Outperforms existing methods on ModelNet40 and ScanObjectNN datasets

Effectively encodes geometric and semantic information in hybrid representations

Utilizes Gumbel-Softmax for differentiable and sparse quantization

Abstract

Vector quantization has emerged as a powerful tool in large-scale multimodal models, unifying heterogeneous representations through discrete token encoding. However, its effectiveness hinges on robust codebook design. Current prototype-based approaches relying on trainable vectors or clustered centroids fall short in representativeness and interpretability, even as multimodal alignment demonstrates its promise in vision-language models. To address these limitations, we propose a simple multimodal prompting-driven quantization framework for point cloud analysis. Our methodology is built upon two core insights: 1) Text embeddings from pre-trained models inherently encode visual semantics through many-to-one contrastive alignment, naturally serving as robust prototype priors; and 2) Multimodal prompts enable adaptive refinement of these prototypes, effectively mitigating vision-language semantic gaps. The framework introduces a dual-constrained quantization space, enforced by compactness and separation regularization, which seamlessly integrates visual and prototype features, resulting in hybrid representations that jointly encode geometric and semantic information. Furthermore, we employ Gumbel-Softmax relaxation to achieve differentiable discretization while maintaining quantization sparsity. Extensive experiments on the ModelNet40 and ScanObjectNN datasets clearly demonstrate the superior effectiveness of the proposed method.