Point Cloud Quantization through Multimodal Prompting for 3D Understanding

• URL: http://arxiv.org/abs/2511.12079v2
• Date: Wed, 19 Nov 2025 07:45:37 GMT
• Title: Point Cloud Quantization through Multimodal Prompting for 3D Understanding
• Authors: Hongxuan Li, Wencheng Zhu, Huiying Xu, Xinzhong Zhu, Pengfei Zhu,
• Abstract summary: We propose a simple multimodal prompting-driven quantization framework for point cloud analysis.<n>Our methodology is built upon two core insights: 1) Text embeddings from pre-trained models inherently encode visual semantics.<n>We employ Gumbel-Softmax relaxation to achieve differentiable discretization while maintaining quantization sparsity.
• Score: 20.397232104616574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Q-BERT4Rec: Quantized Semantic-ID Representation Learning for Multimodal Recommendation [5.699357781063521]
  We propose Q-Bert4Rec, a sequential recommendation framework that unifies semantic representation and quantized modeling.<n>We validate our model on public Amazon benchmarks and demonstrate that Q-Bert4Rec significantly outperforms many strong existing methods.<n>Our source code will be publicly available on GitHub after publishing.
  arXiv  Detail & Related papers  (2025-12-02T07:06:44Z)
• VQRAE: Representation Quantization Autoencoders for Multimodal Understanding, Generation and Reconstruction [83.50898344094153]
  VQRAE produces Continuous semantic features for image understanding and tokens for visual generation within a unified tokenizer.<n>Design enables negligible semantic information for maintaining the ability of multimodal understanding, discrete tokens.<n>VQRAE presents competitive performance on several benchmarks of visual understanding, generation and reconstruction.
  arXiv  Detail & Related papers  (2025-11-28T17:26:34Z)
• Wave-Particle (Continuous-Discrete) Dualistic Visual Tokenization for Unified Understanding and Generation [20.2075487635278]
  Continuous tokenizer (CT) achieves strong performance by bridging multiple understanding modules and generation modules.<n>Discrete tokenizers (DT) offer a conceptually elegant idea by quantizing each image into a primitive.<n>We propose the Continuous-Discrete Dualistic Visual Tokenizer (CDD-VT)
  arXiv  Detail & Related papers  (2025-11-03T13:58:32Z)
• UniAlignment: Semantic Alignment for Unified Image Generation, Understanding, Manipulation and Perception [54.53657134205492]
  UniAlignment is a unified multimodal generation framework within a single diffusion transformer.<n>It incorporates both intrinsic-modal semantic alignment and cross-modal semantic alignment, thereby enhancing the model's cross-modal consistency and instruction-following robustness.<n>We present SemGen-Bench, a new benchmark specifically designed to evaluate multimodal semantic consistency under complex textual instructions.
  arXiv  Detail & Related papers  (2025-09-28T09:11:30Z)
• MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer [90.72238747690972]
  We present Manzano, a simple and scalable unified framework for multimodal large language models.<n>A single vision encoder feeds two adapters that produce continuous embeddings for image-to-text understanding and discrete tokens for text-to-image generation.<n>A unified autoregressive LLM predicts high-level semantics in the form of text and image tokens, with an auxiliary diffusion decoder subsequently translating the image tokens into pixels.
  arXiv  Detail & Related papers  (2025-09-19T17:58:00Z)
• Cross-Layer Discrete Concept Discovery for Interpreting Language Models [13.842670153893977]
  Cross-layer VQ-VAE is a framework that uses vector quantization to map representations across layers.<n>Our approach uniquely combines top-k temperature-based sampling during quantization with EMA codebook updates.
  arXiv  Detail & Related papers  (2025-06-24T22:43:36Z)
• Show-o2: Improved Native Unified Multimodal Models [57.34173415412808]
  Show-o2 is a native unified multimodal models that leverage autoregressive modeling and flow matching.<n>Built upon a 3D causal variational autoencoder space, unified visual representations are constructed through a dual-path of spatial (-temporal) fusion.
  arXiv  Detail & Related papers  (2025-06-18T15:39:15Z)
• Proto-FG3D: Prototype-based Interpretable Fine-Grained 3D Shape Classification [59.68055837500357]
  We propose the first prototype-based framework named Proto-FG3D for fine-grained 3D shape classification.<n>Proto-FG3D establishes joint multi-view and multi-category representation learning via Prototype Association.<n>Proto-FG3D surpasses state-of-the-art methods in accuracy, transparent predictions, and ad-hoc interpretability with visualizations.
  arXiv  Detail & Related papers  (2025-05-23T09:31:02Z)
• BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation [15.818669767036592]
  We propose a Behavior-Bind multi-modal Quantization for Sequential Recommendation (BBQRec) featuring dual-aligned quantization and semantics-aware sequence modeling.<n>BBQRec disentangles modality-agnostic behavioral patterns from noisy modality-specific features through contrastive codebook learning.<n>We design a discretized similarity reweighting mechanism that dynamically adjusts self-attention scores using quantized semantic relationships.
  arXiv  Detail & Related papers  (2025-04-09T07:19:48Z)
• Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment [16.733970553781887]
  Recent findings suggest high semantic similarity between well-trained unimodal encoders.<n>We propose a novel framework that aligns vision and language using frozen unimodal encoders.
  arXiv  Detail & Related papers  (2024-09-28T17:57:32Z)
• FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers [55.2480439325792]
  We propose FUSE, an approach to approximating an adapter layer that maps from one model's textual embedding space to another, even across different tokenizers. We show the efficacy of our approach via multi-objective optimization over vision-language and causal language models for image captioning and sentiment-based image captioning.
  arXiv  Detail & Related papers  (2024-08-09T02:16:37Z)
• Multi-Modal Prototypes for Open-World Semantic Segmentation [37.84805778548119]
  We propose to encompass textual and visual clues as multi-modal prototypes to allow more comprehensive support for semantic segmentation. We decompose the high-level language information as multi-aspect prototypes and aggregate the low-level visual information as more semantic prototypes. Based on an elastic mask prediction module, we are able to solve the zero-shot, few-shot and generalized counterpart tasks in one architecture.
  arXiv  Detail & Related papers  (2023-07-05T03:27:31Z)
• Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck [52.08901549360262]
  Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. VAEs tend to ignore latent variables with a strong auto-regressive decoder. We propose a principled approach to enforce an implicit latent feature matching in a more compact latent space.
  arXiv  Detail & Related papers  (2020-04-22T14:41:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.