  Vision Language Models (VLMs) have demonstrated remarkable performance in 2D
vision and language tasks. However, their ability to reason about spatial
arrangements remains limited. In this work, we introduce Spatial Region GPT
(SpatialRGPT) to enhance VLMs' spatial perception and reasoning capabilities.
SpatialRGPT advances VLMs' spatial understanding through two key innovations:
(1) a data curation pipeline that enables effective learning of regional
representation from 3D scene graphs, and (2) a flexible plugin module for
integrating depth information into the visual encoder of existing VLMs. During
inference, when provided with user-specified region proposals, SpatialRGPT can
accurately perceive their relative directions and distances. Additionally, we
propose SpatialRGBT-Bench, a benchmark with ground-truth 3D annotations
encompassing indoor, outdoor, and simulated environments, for evaluating 3D
spatial cognition in VLMs. Our results demonstrate that SpatialRGPT
significantly enhances performance in spatial reasoning tasks, both with and
without local region prompts. The model also exhibits strong generalization
capabilities, effectively reasoning about complex spatial relations and
functioning as a region-aware dense reward annotator for robotic tasks. Code,
dataset, and benchmark will be released at
https://www.anjiecheng.me/SpatialRGPT
