ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in
  Vision-Language Models
Vision-language models (VLMs) have demonstrated remarkable capabilities in
understanding and reasoning about visual content, but significant challenges
persist in tasks requiring cross-viewpoint understanding and spatial reasoning.
We identify a critical limitation: current VLMs excel primarily at egocentric
spatial reasoning (from the camera's perspective) but fail to generalize to
allocentric viewpoints when required to adopt another entity's spatial frame of
reference. We introduce ViewSpatial-Bench, the first comprehensive benchmark
designed specifically for multi-viewpoint spatial localization recognition
evaluation across five distinct task types, supported by an automated 3D
annotation pipeline that generates precise directional labels. Comprehensive
evaluation of diverse VLMs on ViewSpatial-Bench reveals a significant
performance disparity: models demonstrate reasonable performance on
camera-perspective tasks but exhibit reduced accuracy when reasoning from a
human viewpoint. By fine-tuning VLMs on our multi-perspective spatial dataset,
we achieve an overall performance improvement of 46.24% across tasks,
highlighting the efficacy of our approach. Our work establishes a crucial
benchmark for spatial intelligence in embodied AI systems and provides
empirical evidence that modeling 3D spatial relationships enhances VLMs'
corresponding spatial comprehension capabilities.
6836962225d0c6bd7c9186fa
Dingming Li
6836962225d0c6bd7c9186fb
Hongxing Li
admin_assigned
2025-05-28T12:49:45.514Z
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Hongxing Li
user
Gino319
6836962225d0c6bd7c9186fc
Zixuan Wang
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Yuchen Yan
claimed_verified
2025-05-28T12:12:15.430Z
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Yuchen Yan
user
yanyc
6836962225d0c6bd7c9186fe
Hang Zhang
6836962225d0c6bd7c9186ff
Siqi Chen
claimed_verified
2025-06-05T07:28:48.582Z
676b86e79ff0244316f7202f
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chensiqi
user
xiaoooobai
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Guiyang Hou
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Shengpei Jiang
6836962225d0c6bd7c918702
Wenqi Zhang
6836962225d0c6bd7c918703
Yongliang Shen
admin_assigned
2025-05-28T12:55:16.597Z
5e1058e9fcf41d740b69966d
/avatars/ce74839ba871f2b54313a670a233ba82.svg
Yongliang Shen
user
tricktreat
6836962225d0c6bd7c918704
Weiming Lu
6836962225d0c6bd7c918705
Yueting Zhuang
ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in
  Vision-Language Models
Vision-language models (VLMs) have demonstrated remarkable capabilities in
understanding and reasoning about visual content, but significant challenges
persist in tasks requiring cross-viewpoint understanding and spatial reasoning.
We identify a critical limitation: current VLMs excel primarily at egocentric
spatial reasoning (from the camera's perspective) but fail to generalize to
allocentric viewpoints when required to adopt another entity's spatial frame of
reference. We introduce ViewSpatial-Bench, the first comprehensive benchmark
designed specifically for multi-viewpoint spatial localization recognition
evaluation across five distinct task types, supported by an automated 3D
annotation pipeline that generates precise directional labels. Comprehensive
evaluation of diverse VLMs on ViewSpatial-Bench reveals a significant
performance disparity: models demonstrate reasonable performance on
camera-perspective tasks but exhibit reduced accuracy when reasoning from a
human viewpoint. By fine-tuning VLMs on our multi-perspective spatial dataset,
we achieve an overall performance improvement of 46.24% across tasks,
highlighting the efficacy of our approach. Our work establishes a crucial
benchmark for spatial intelligence in embodied AI systems and provides
empirical evidence that modeling 3D spatial relationships enhances VLMs'
corresponding spatial comprehension capabilities.
A new benchmark, ViewSpatial-Bench, evaluates VLMs on multi-viewpoint spatial reasoning, revealing performance gaps that are mitigated with fine-tuning on 3D spatial datasets.
Vision-language models ViewSpatial-Bench multi-viewpoint spatial localization allocentric viewpoints egocentric spatial reasoning 3D spatial relationships spatial intelligence embodied AI systems