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cs.CV2025

MMSI-Video-Bench: A Holistic Benchmark for Video-Based Spatial Intelligence

Jingli Lin, Runsen Xu, Shaohao Zhu +11

Spatial understanding over continuous visual input is crucial for MLLMs to evolve into general-purpose assistants in physical environments. Yet there is still no comprehensive benc…

cs.CV2025

GVLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning

Wenbo Hu, Jingli Lin, Yilin Long +7

Vision-Language Models (VLMs) still lack robustness in spatial intelligence, demonstrating poor performance on spatial understanding and reasoning tasks. We attribute this gap to t…

cs.CV2025

InternScenes: A Large-scale Simulatable Indoor Scene Dataset with Realistic Layouts

Weipeng Zhong, Peizhou Cao, Yichen Jin +9

The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts. However, existing datasets typic…

cs.CV2025

OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding

Jingli Lin, Chenming Zhu, Runsen Xu +4

Recent advances in multimodal large language models (MLLMs) have shown remarkable capabilities in integrating vision and language for complex reasoning. While most existing benchma…

cs.CV2025

MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence

Sihan Yang, Runsen Xu, Yiman Xie +10

Spatial intelligence is essential for multimodal large language models (MLLMs) operating in the complex physical world. Existing benchmarks, however, probe only single-image relati…