How Far are VLMs from Visual Spatial Intelligence? A Benchmark-Driven
  Perspective
Visual Spatial Reasoning (VSR) is a core human cognitive ability and a
critical requirement for advancing embodied intelligence and autonomous
systems. Despite recent progress in Vision-Language Models (VLMs), achieving
human-level VSR remains highly challenging due to the complexity of
representing and reasoning over three-dimensional space. In this paper, we
present a systematic investigation of VSR in VLMs, encompassing a review of
existing methodologies across input modalities, model architectures, training
strategies, and reasoning mechanisms. Furthermore, we categorize spatial
intelligence into three levels of capability, ie, basic perception, spatial
understanding, spatial planning, and curate SIBench, a spatial intelligence
benchmark encompassing nearly 20 open-source datasets across 23 task settings.
Experiments with state-of-the-art VLMs reveal a pronounced gap between
perception and reasoning, as models show competence in basic perceptual tasks
but consistently underperform in understanding and planning tasks, particularly
in numerical estimation, multi-view reasoning, temporal dynamics, and spatial
imagination. These findings underscore the substantial challenges that remain
in achieving spatial intelligence, while providing both a systematic roadmap
and a comprehensive benchmark to drive future research in the field. The
related resources of this study are accessible at
https://sibench.github.io/Awesome-Visual-Spatial-Reasoning/.
68d3474a0e215259d193b175
Songsong Yu
claimed_verified
2025-09-24T13:53:37.350Z
664da5a094234f3c17df8d3b
/avatars/db8f6988ae7ec289a210a747c54470ad.svg
Songsong Yu
user
Two-hot
68d3474a0e215259d193b176
Yuxin Chen
claimed_verified
2025-09-24T13:53:34.885Z
669f3b098c65c172c4d64039
/avatars/d85158964853ab87b9b677fa16df90f8.svg
Yuxin Chen
user
Uasonchen
68d3474a0e215259d193b177
Hao Ju
68d3474a0e215259d193b178
Lianjie Jia
68d3474a0e215259d193b179
Fuxi Zhang
68d3474a0e215259d193b17a
Shaofei Huang
68d3474a0e215259d193b17b
Yuhan Wu
68d3474a0e215259d193b17c
Rundi Cui
68d3474a0e215259d193b17d
Binghao Ran
68d3474a0e215259d193b17e
Zaibin Zhang
68d3474a0e215259d193b17f
Zhedong Zheng
68d3474a0e215259d193b180
Zhipeng Zhang
68d3474a0e215259d193b181
Yifan Wang
68d3474a0e215259d193b182
Lin Song
68d3474a0e215259d193b183
Lijun Wang
68d3474a0e215259d193b184
Yanwei Li
68d3474a0e215259d193b185
Ying Shan
68d3474a0e215259d193b186
Huchuan Lu
How Far are VLMs from Visual Spatial Intelligence? A Benchmark-Driven
  Perspective
Visual Spatial Reasoning (VSR) is a core human cognitive ability and a
critical requirement for advancing embodied intelligence and autonomous
systems. Despite recent progress in Vision-Language Models (VLMs), achieving
human-level VSR remains highly challenging due to the complexity of
representing and reasoning over three-dimensional space. In this paper, we
present a systematic investigation of VSR in VLMs, encompassing a review of
existing methodologies across input modalities, model architectures, training
strategies, and reasoning mechanisms. Furthermore, we categorize spatial
intelligence into three levels of capability, ie, basic perception, spatial
understanding, spatial planning, and curate SIBench, a spatial intelligence
benchmark encompassing nearly 20 open-source datasets across 23 task settings.
Experiments with state-of-the-art VLMs reveal a pronounced gap between
perception and reasoning, as models show competence in basic perceptual tasks
but consistently underperform in understanding and planning tasks, particularly
in numerical estimation, multi-view reasoning, temporal dynamics, and spatial
imagination. These findings underscore the substantial challenges that remain
in achieving spatial intelligence, while providing both a systematic roadmap
and a comprehensive benchmark to drive future research in the field. The
related resources of this study are accessible at
https://sibench.github.io/Awesome-Visual-Spatial-Reasoning/.
Research investigates visual spatial reasoning in vision-language models, highlighting gaps between perceptual and reasoning capabilities, and introduces SIBench as a benchmark for future research.
Visual Spatial Reasoning Vision-Language Models spatial intelligence basic perception spatial understanding spatial planning SIBench numerical estimation multi-view reasoning temporal dynamics spatial imagination