title: SpatialScore: Towards Unified Evaluation for Multimodal Spatial
    Understanding
summary: 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.
paper_authors._id: 682fde942f8f73559fcbc5da
paper_authors.name: Haoning Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:16:33.137Z
paper_authors._id: 632c7a0d1d303f5f9acf01b8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/632c7a0d1d303f5f9acf01b8/T010IFuCp6UaOeIyWhbCk.jpeg
paper_authors.fullname: Haoning Wu
paper_authors.type: user
paper_authors.user: haoningwu
paper_authors._id: 682fde942f8f73559fcbc5db
paper_authors.name: Xiao Huang
paper_authors._id: 682fde942f8f73559fcbc5dc
paper_authors.name: Yaohui Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T09:01:14.383Z
paper_authors._id: 662377137a1a338358811138
paper_authors.avatarUrl: /avatars/f6cac3dc1352a05618bcc1eb168b3ef4.svg
paper_authors.fullname: Yaohui Chen
paper_authors.type: user
paper_authors.user: YaohuiChen
paper_authors._id: 682fde942f8f73559fcbc5dd
paper_authors.name: Ya Zhang
paper_authors._id: 682fde942f8f73559fcbc5de
paper_authors.name: Yanfeng Wang
paper_authors._id: 682fde942f8f73559fcbc5df
paper_authors.name: Weidi Xie
paper_title: SpatialScore: Towards Unified Evaluation for Multimodal Spatial
    Understanding
paper_summary: 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.
paper_ai_summary: SpatialScore benchmarks multimodal large language models for 3D spatial understanding, revealing challenges and showcasing the effectiveness of SpatialAgent with specialized tools.
paper_ai_keywords: Multimodal large language models MLLMs VGBench SpatialScore spatial understanding visual geometry perception camera pose motion estimation multi-agent system SpatialAgent Plan-Execute ReAct reasoning paradigms