VLM3: 视觉语言模型是原生的3D学习者

摘要

视觉语言模型（VLM）能够通过提示实现统一模型解决多种视觉任务，在语义理解方面已展现出优异性能。然而，3D理解仍主要依赖具有复杂任务特定设计的专家视觉模型。本文的核心论点是：VLM本质上是3D学习者。我们深入的大规模研究表明：1）焦距统一、2）基于文本的像素参考和3）数据混合与缩放，是实现高效3D学习的全部必要条件。模型架构变更、大模型、强数据增强以及包含回归公式在内的复杂损失——这些构成专家视觉模型基础的众多要素，实际上并非必要条件。为此，我们提出VLM3——一种具备最简设计的可扩展方法，使标准VLM能够掌握多样化的3D任务。VLM3不仅将VLM深度估计精度大幅提升（从0.84提升至0.9），还支持像素对应、相机姿态估计和物体级3D理解等多种3D任务，在保持标准架构和基于文本训练的同时，达到专家视觉模型的精度。我们相信VLM3为简单且可扩展的3D学习开辟了新范式。

English

Vision Language Models (VLMs) enable a unified model to solve various vision tasks through prompting. They have shown promising performance in semantic understanding. However, 3D understanding still largely relies on expert vision models with complex task-specific designs. The key argument this work wants to make is that VLMs are native 3D learners. Our in-depth large scale study shows that 1) focal length unification, 2) text-based pixel reference and 3) data mixture and scaling, are all you need for effective 3D learning. Model architecture changes, large models, heavy data augmentations, and complex losses including the regression formulation, many of which form the foundation of expert vision models, are actually not necessary conditions. As a result, we propose VLM3, a scalable method with the simplest design that enables standard VLMs to master diverse 3D tasks. VLM3 not only advances the VLM depth estimation accuracy by a large margin (0.84 -> 0.9), but also enables diverse 3D tasks such as pixel correspondence, camera pose estimation and object-level 3D understanding, matching expert vision model accuracy while maintaining standard architectures and text-based training. We believe VLM3 opens up a new paradigm for simple and scalable 3D learning.