OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for
  Vision Language Models
Authors: name: Mengdi Jia, name: Zekun Qi, name: Shaochen Zhang, name: Wenyao Zhang, name: Xinqiang Yu, name: Jiawei He, name: He Wang, name: Li Yi

Spatial reasoning is a key aspect of cognitive psychology and remains a major
bottleneck for current vision-language models (VLMs). While extensive research
has aimed to evaluate or improve VLMs' understanding of basic spatial
relations, such as distinguishing left from right, near from far, and object
counting, these tasks represent only the most fundamental level of spatial
reasoning. In this work, we introduce OmniSpatial, a comprehensive and
challenging benchmark for spatial reasoning, grounded in cognitive psychology.
OmniSpatial covers four major categories: dynamic reasoning, complex spatial
logic, spatial interaction, and perspective-taking, with 50 fine-grained
subcategories. Through Internet data crawling and careful manual annotation, we
construct over 1.5K question-answer pairs. Extensive experiments show that both
open- and closed-source VLMs, as well as existing reasoning and spatial
understanding models, exhibit significant limitations in comprehensive spatial
understanding. We further analyze failure cases and propose potential
directions for future research.