Multimodal Spatial Reasoning in the Large Model Era: A Survey and
  Benchmarks
Authors: name: Xu Zheng; name: Zihao Dongfang; name: Lutao Jiang; name: Boyuan Zheng; name: Yulong Guo; name: Zhenquan Zhang; name: Giuliano Albanese; name: Runyi Yang; name: Mengjiao Ma; name: Zixin Zhang; name: Chenfei Liao; name: Dingcheng Zhen; name: Yuanhuiyi Lyu; name: Yuqian Fu; name: Bin Ren; name: Linfeng Zhang; name: Danda Pani Paudel; name: Nicu Sebe; name: Luc Van Gool; name: Xuming Hu
Published: 2025-10-29 17:55:43

Abstract: Humans possess spatial reasoning abilities that enable them to understand
spaces through multimodal observations, such as vision and sound. Large
multimodal reasoning models extend these abilities by learning to perceive and
reason, showing promising performance across diverse spatial tasks. However,
systematic reviews and publicly available benchmarks for these models remain
limited. In this survey, we provide a comprehensive review of multimodal
spatial reasoning tasks with large models, categorizing recent progress in
multimodal large language models (MLLMs) and introducing open benchmarks for
evaluation. We begin by outlining general spatial reasoning, focusing on
post-training techniques, explainability, and architecture. Beyond classical 2D
tasks, we examine spatial relationship reasoning, scene and layout
understanding, as well as visual question answering and grounding in 3D space.
We also review advances in embodied AI, including vision-language navigation
and action models. Additionally, we consider emerging modalities such as audio
and egocentric video, which contribute to novel spatial understanding through
new sensors. We believe this survey establishes a solid foundation and offers
insights into the growing field of multimodal spatial reasoning. Updated
information about this survey, codes and implementation of the open benchmarks
can be found at https://github.com/zhengxuJosh/Awesome-Spatial-Reasoning.