SpatialScore: Towards Unified Evaluation for Multimodal Spatial
  Understanding
Authors: name: Haoning Wu, name: Xiao Huang, name: Yaohui Chen, name: Ya Zhang, name: Yanfeng Wang, name: Weidi Xie

Multimodal large language models (MLLMs) have achieved impressive success in
question-answering tasks, yet their capabilities for spatial understanding are
less explored. This work investigates a critical question: do existing MLLMs
possess 3D spatial perception and understanding abilities? Concretely, we make
the following contributions in this paper: (i) we introduce VGBench, a
benchmark specifically designed to assess MLLMs for visual geometry perception,
e.g., camera pose and motion estimation; (ii) we propose SpatialScore, the most
comprehensive and diverse multimodal spatial understanding benchmark to date,
integrating VGBench with relevant data from the other 11 existing datasets.
This benchmark comprises 28K samples across various spatial understanding
tasks, modalities, and QA formats, along with a carefully curated challenging
subset, SpatialScore-Hard; (iii) we develop SpatialAgent, a novel multi-agent
system incorporating 9 specialized tools for spatial understanding, supporting
both Plan-Execute and ReAct reasoning paradigms; (iv) we conduct extensive
evaluations to reveal persistent challenges in spatial reasoning while
demonstrating the effectiveness of SpatialAgent. We believe SpatialScore will
offer valuable insights and serve as a rigorous benchmark for the next
evolution of MLLMs.