SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models
On the map: LLM reasoning → Spatial and embodied reasoning
Abstract (arXiv)
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 are released at https://www.anjiecheng.me/SpatialRGPT
Key facts. Cited by 297 papers in the map (data through 7 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 75 of its 99 references that are in the map. First posted on arXiv 3 Jun 2024.
Builds on
75 of 99 references.
Closest ancestors · 3
Same direction, newest first
- 2024PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs12 Feb
- 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities22 Jan
- 20233D Concept Learning and Reasoning from Multi-View Images20 Mar
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Citing papers per year: 2024: 26; 2025: 140; 2026: 131.
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