3D-R1: Enhancing Reasoning in 3D VLMs for Unified Scene Understanding
Authors: name: Ting Huang, name: Zeyu Zhang, name: Hao Tang
Organization: Peking University

Large vision-language models (VLMs) have made significant strides in 2D
visual understanding tasks, sparking interest in extending these capabilities
to 3D scene understanding. However, current 3D VLMs often struggle with robust
reasoning and generalization due to limitations in high-quality spatial data
and the static nature of viewpoint assumptions. To address these challenges, we
propose 3D-R1, a foundation model that enhances the reasoning capabilities of
3D VLMs. Specifically, we first construct a high-quality synthetic dataset with
CoT, named Scene-30K, leveraging existing 3D-VL datasets and a data engine
based on Gemini 2.5 Pro. It serves as cold-start initialization data for 3D-R1.
Moreover, we leverage RLHF policy such as GRPO in the reinforcement learning
training process to enhance reasoning capabilities and introduce three reward
functions: a perception reward, a semantic similarity reward and a format
reward to maintain detection accuracy and answer semantic precision.
Furthermore, we introduce a dynamic view selection strategy that adaptively
chooses the most informative perspectives for 3D scene understanding. Extensive
experiments demonstrate that 3D-R1 delivers an average improvement of 10%
across various 3D scene benchmarks, highlighting its effectiveness in enhancing
reasoning and generalization in 3D scene understanding. Code:
https://github.com/AIGeeksGroup/3D-R1. Website:
https://aigeeksgroup.github.io/3D-R1.