title: ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in
    Vision-Language Models
summary: 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.
paper_authors._id: 6836962225d0c6bd7c9186fa
paper_authors.name: Dingming Li
paper_authors._id: 6836962225d0c6bd7c9186fb
paper_authors.name: Hongxing Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:49:45.514Z
paper_authors._id: 67505c291372c3446d0a1f5d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/rOVvWED4Fm6e8aWJDP5pj.jpeg
paper_authors.fullname: Hongxing Li
paper_authors.type: user
paper_authors.user: Gino319
paper_authors._id: 6836962225d0c6bd7c9186fc
paper_authors.name: Zixuan Wang
paper_authors._id: 6836962225d0c6bd7c9186fd
paper_authors.name: Yuchen Yan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T12:12:15.430Z
paper_authors._id: 64098738342c26884c792c93
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64098738342c26884c792c93/SxBUd-wLrl-PjQsrVYJte.jpeg
paper_authors.fullname: Yuchen Yan
paper_authors.type: user
paper_authors.user: yanyc
paper_authors._id: 6836962225d0c6bd7c9186fe
paper_authors.name: Hang Zhang
paper_authors._id: 6836962225d0c6bd7c9186ff
paper_authors.name: Siqi Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:28:48.582Z
paper_authors._id: 676b86e79ff0244316f7202f
paper_authors.avatarUrl: /avatars/3e1d26312a96752356895ab88eeb3ce0.svg
paper_authors.fullname: chensiqi
paper_authors.type: user
paper_authors.user: xiaoooobai
paper_authors._id: 6836962225d0c6bd7c918700
paper_authors.name: Guiyang Hou
paper_authors._id: 6836962225d0c6bd7c918701
paper_authors.name: Shengpei Jiang
paper_authors._id: 6836962225d0c6bd7c918702
paper_authors.name: Wenqi Zhang
paper_authors._id: 6836962225d0c6bd7c918703
paper_authors.name: Yongliang Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:55:16.597Z
paper_authors._id: 5e1058e9fcf41d740b69966d
paper_authors.avatarUrl: /avatars/ce74839ba871f2b54313a670a233ba82.svg
paper_authors.fullname: Yongliang Shen
paper_authors.type: user
paper_authors.user: tricktreat
paper_authors._id: 6836962225d0c6bd7c918704
paper_authors.name: Weiming Lu
paper_authors._id: 6836962225d0c6bd7c918705
paper_authors.name: Yueting Zhuang
paper_title: ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in
    Vision-Language Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Vision-language models ViewSpatial-Bench multi-viewpoint spatial localization allocentric viewpoints egocentric spatial reasoning 3D spatial relationships spatial intelligence embodied AI systems