Point Cloud Quantization through Multimodal Prompting for 3D Understanding

TL;DR

This work tackles semantic gaps in 3D point cloud understanding by grounding visual features in language-derived prototypes. It introduces PCQ, a text-guided, multimodal prompting framework that discretizes continuous point-cloud features into a shared prototype space using Gumbel-Softmax, and fuses them with visual features through cross-modal attention. The method employs dual regularizations—compactness to tighten intra-class variation and separation to maximize inter-class distinctness—alongside adaptive textual prompts to refine prototypes, achieving strong results on ModelNet40 and ScanObjectNN with high parameter efficiency. The approach demonstrates robustness in few-shot and cross-dataset settings and offers interpretable prototypes with high semantic alignment, signaling practical impact for scalable, multimodal 3D understanding.

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.

Figures (6)

• Figure 1: a) Cluster centroids as prototypes and b) Trainable codebooks as prototypes suffer from inaccurate clustering and domain shift, which reduces their representativeness and generalization. c) Our method leverages a pre-trained vision-language model to derive text-driven semantic prototypes, refined during fine-tuning to enhance representativeness, interpretability, and generalization for 3D understanding.
• Figure 2: Framework of the proposed approach. Our method comprises feature extraction and point cloud quantization modules. The feature extraction module uses ULIP-2 text encoder and 3D point cloud encoder to extract text and point cloud features. The quantization module then takes these text features as prototypes and quantizes point cloud features into prototype features. To enable differentiable sampling, discrete features are modeled through a Gumbel distribution, and Gumbel-Softmax reparameterization is adopted to represent point cloud features with prototype features. Finally, point cloud features are combined with prototype features via cross-modal feature fusion to produce the final hybrid representation. Notably, parameter-efficient fine-tuning is employed to optimize both prototype and point cloud features, constrained by compactness and separation losses.
• Figure 3: Data efficiency comparison. Models are trained on varying percentages of data and evaluated on the full test set.
• Figure 5: The t-SNE visualization before () and after () model fine-tuning across four datasets. Dashed lines connect corresponding prototypes across training phases.
• Figure 6: Label augmentation by employing the large language model GPT-4 to provide enriched textual descriptions.