Point Cloud as a Foreign Language for Multi-modal Large Language Model

2026-03-10

AI Summary

This work proposes SAGE, the first end-to-end 3D multimodal large language model that operates directly on raw point clouds, addressing key limitations of existing approaches that rely on pretrained 3D encoders—namely semantic misalignment, sensitivity to input resolution, and high computational overhead. SAGE treats point clouds as a “foreign language” and employs a lightweight 3D tokenizer comprising geometric sampling, neighborhood aggregation, and vector quantization to map them into discrete tokens. To enhance complex 3D reasoning, the model incorporates a preference optimization strategy guided by a semantic alignment reward. Experimental results demonstrate that SAGE outperforms current methods across multiple 3D understanding benchmarks, while exhibiting superior computational efficiency, stronger large language model generalization, and robustness to variations in input resolution.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: 3D Computer Vision
• Natural Language Processing: (Large) Language Models

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Multi-modal large language models (MLLMs) have shown remarkable progress in integrating visual and linguistic understanding. Recent efforts have extended these capabilities to 3D understanding through encoder-based architectures that rely on pre-trained 3D encoders to extract geometric features. However, such approaches suffer from semantic misalignment between geometric and linguistic spaces, resolution sensitivity, and substantial computational overhead. In this work, we present SAGE, the first end-to-end 3D MLLM that directly processes raw point clouds without relying on a pre-trained 3D encoder. Our approach introduces a lightweight 3D tokenizer that combines geometric sampling and neighbourhood aggregation with vector quantization to convert point clouds into discrete tokens--treating 3D data as a foreign language that naturally extends the LLM's vocabulary. Furthermore, to enhance the model's reasoning capability on complex 3D tasks, we propose a preference optimization training strategy with a semantic alignment-based reward, specifically designed for open-ended 3D question answering where responses are descriptive. Extensive experiments across diverse 3D understanding benchmarks demonstrate that our end-to-end approach outperforms existing encoder-based methods while offering significant advantages in computational efficiency, generalization across LLM backbones, and robustness to input resolution variations. Code is available at: github.com/snehaputul/SAGE3D.

Problem

• point cloud
• multi-modal large language model
• semantic misalignment
• resolution sensitivity
• computational overhead

Innovation

• point cloud tokenization
• end-to-end 3D MLLM
• semantic alignment
• preference optimization
• vector quantization