Multimodal Spatial Reasoning in the Large Model Era: A Survey and
  Benchmarks
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.
6902f25c72739622ee92a8c4
Xu Zheng
6902f25c72739622ee92a8c5
Zihao Dongfang
claimed_verified
2026-03-19T13:06:00.071Z
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DONGFANG ZIHAO
UUUserna
user
UUUserna
6902f25c72739622ee92a8c6
Lutao Jiang
6902f25c72739622ee92a8c7
Boyuan Zheng
6902f25c72739622ee92a8c8
Yulong Guo
6902f25c72739622ee92a8c9
Zhenquan Zhang
6902f25c72739622ee92a8ca
Giuliano Albanese
6902f25c72739622ee92a8cb
Runyi Yang
6902f25c72739622ee92a8cc
Mengjiao Ma
6902f25c72739622ee92a8cd
Zixin Zhang
claimed_verified
2025-10-30T14:16:28.822Z
674aa9af9494dd9106006c27
/avatars/2f04bb009983ec0ade738aa7941cf6dc.svg
Zixin Zhang
zhangzixin02
user
zhangzixin02
6902f25c72739622ee92a8ce
Chenfei Liao
claimed_verified
2026-03-26T14:05:57.952Z
6806464ed918f6d2fee2bc8b
https://cdn-avatars.huggingface.co/v1/production/uploads/6806464ed918f6d2fee2bc8b/rgpG2oO0m6PT0KltCF_Wf.jpeg
Chenfei Liao
Chenfei-Liao
user
Chenfei-Liao
6902f25c72739622ee92a8cf
Dingcheng Zhen
6902f25c72739622ee92a8d0
Yuanhuiyi Lyu
6902f25c72739622ee92a8d1
Yuqian Fu
6902f25c72739622ee92a8d2
Bin Ren
6902f25c72739622ee92a8d3
Linfeng Zhang
6902f25c72739622ee92a8d4
Danda Pani Paudel
6902f25c72739622ee92a8d5
Nicu Sebe
6902f25c72739622ee92a8d6
Luc Van Gool
6902f25c72739622ee92a8d7
Xuming Hu
Multimodal Spatial Reasoning in the Large Model Era: A Survey and
  Benchmarks
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.
A survey of multimodal spatial reasoning tasks and models, focusing on large language models, post-training techniques, explainability, architecture, and emerging modalities like audio and egocentric video.
multimodal reasoning models large language models post-training techniques explainability architecture spatial relationship reasoning scene and layout understanding visual question answering grounding in 3D space embodied AI vision-language navigation action models audio egocentric video