← 研踪 PaperTrailCVF · CVPR 2026:cvf:5e95a506d9e91d89088f4a3f8cda62e4 · 记录 #1
SpatialScore: Towards Comprehensive Evaluation for Spatial Intelligence
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:30
摘要
Existing evaluations of multimodal large language models (MLLMs) on spatial intelligence are typically fragmented and limited in scope. In this work, we conduct a holistic assessment of the spatial understanding abilities of modern MLLMs and propose complementary data-driven and agent-based solutions. Concretely, we make the following contributions: (i) we propose SpatialScore, the most comprehensive and diverse multimodal spatial intelligence benchmark to date, encompassing various visual data types, input modalities, and question-answering formats with approximately 5K manually verified samples across 30 distinct tasks; (ii) we construct SpatialCorpus, a large-scale training resource with 331K multimodal QA samples for supervised fine-tuning Qwen3-VL on spatial understanding; (iii) we develop SpaitalAgent, a multi-agent system incorporating 12 specialized spatial perception tools, supporting both Plan-Execute and ReAct reasoning paradigms, enabling to improve spatial reasoning in a training-free manner; and (iv) we conduct extensive evaluations on 40 representative MLLMs, revealing persistent challenges in spatial intelligence while demonstrating the effectiveness of our data-driven and agent-based solutions. All data, code, and models will be publicly available.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
SpatialScore: Towards Comprehensive Evaluation for Spatial Intelligence
Existing evaluations of multimodal large language models (MLLMs) on spatial intelligence are typically fragmented and limited in scope. In this work, we conduct a holistic assessment of the spatial understanding abilities of modern MLLMs and propose complementary data-driven and agent-based solutions. Concretely, we make the following contributions: (i) we propose SpatialScore, the most comprehensive and diverse multimodal spatial intelligence benchmark to date, encompassing various visual data types, input modalities, and question-answering formats with approximately 5K manually verified samples across 30 distinct tasks; (ii) we construct SpatialCorpus, a large-scale training resource with 331K multimodal QA samples for supervised fine-tuning Qwen3-VL on spatial understanding; (iii) we develop SpaitalAgent, a multi-agent system incorporating 12 specialized spatial perception tools, supporting both Plan-Execute and ReAct reasoning paradigms, enabling to improve spatial reasoning in a training-free manner; and (iv) we conduct extensive evaluations on 40 representative MLLMs, revealing persistent challenges in spatial intelligence while demonstrating the effectiveness of our data-driven and agent-based solutions. All data, code, and models will be publicly available.
查看此版本解读 →查看此版本原文 ↗