  Top-view perspective denotes a typical way in which humans read and reason
over different types of maps, and it is vital for localization and navigation
of humans as well as of `non-human' agents, such as the ones backed by large
Vision-Language Models (VLMs). Nonetheless, spatial reasoning capabilities of
modern VLMs remain unattested and underexplored. In this work, we thus study
their capability to understand and reason over spatial relations from the top
view. The focus on top view also enables controlled evaluations at different
granularity of spatial reasoning; we clearly disentangle different abilities
(e.g., recognizing particular objects versus understanding their relative
positions). We introduce the TopViewRS (Top-View Reasoning in Space) dataset,
consisting of 11,384 multiple-choice questions with either realistic or
semantic top-view map as visual input. We then use it to study and evaluate
VLMs across 4 perception and reasoning tasks with different levels of
complexity. Evaluation of 10 representative open- and closed-source VLMs
reveals the gap of more than 50% compared to average human performance, and it
is even lower than the random baseline in some cases. Although additional
experiments show that Chain-of-Thought reasoning can boost model capabilities
by 5.82% on average, the overall performance of VLMs remains limited. Our
findings underscore the critical need for enhanced model capability in top-view
spatial reasoning and set a foundation for further research towards human-level
proficiency of VLMs in real-world multimodal tasks.
