💡 The paper VLM3 Vision Language Models Are Native 3D Learners presents a study that challenges the common approach to 3D understanding tasks in computer vision. Typically these tasks rely on specialized vision models with complex designs and extensive data augmentation. However the authors argue that vision language models can be adapted for 3D understanding tasks through simple architectural modifications and text-based training.

The problem addressed in this paper is that 3D understanding tasks such as depth estimation and object-level 3D understanding are currently dominated by expert vision models that have complex task-specific designs. The authors propose that vision language models can be native 3D learners and achieve comparable performance to these specialized models.

The method used in this study involves making three simple modifications to standard vision language models. These modifications include focal length unification, text-based pixel reference, and data mixture and scaling. The authors propose VLM3, a scalable method that enables standard vision language models to master diverse 3D tasks without requiring complex designs or extensive data augmentation.

The results of the study show that VLM3 advances the depth estimation accuracy of vision language models by a large margin, from 0.84 to 0.9. Additionally, VLM3 enables diverse 3D tasks such as pixel correspondence, camera pose estimation, and object-level 3D understanding, matching the accuracy of expert vision models while maintaining standard architectures and text-based training. Overall, the paper presents a new paradigm for simple and scalable 3D learning, demonstrating that vision language models can be effective native 3D learners.