ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in
  Vision-Language Models
Authors: name: Dingming Li, name: Hongxing Li, name: Zixuan Wang, name: Yuchen Yan, name: Hang Zhang, name: Siqi Chen, name: Guiyang Hou, name: Shengpei Jiang, name: Wenqi Zhang, name: Yongliang Shen, name: Weiming Lu, name: Yueting Zhuang

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.