8 papers
GTAM: Geometry Grounded Track Anything Model
Chenming Zhu, Peizhou Cao, Jingli Lin +5
Human spatial understanding arises from jointly perceiving geometry and semantics, enabling consistent object identification and localization across viewpoints and time. Current vi…
Thinking with Imagination: Agentic Visual Spatial Reasoning with World Simulators
Chenming Zhu, Jingli Lin, Yilin Long +4
While Vision-Language Models (VLMs) have shown strong visual reasoning capabilities, their spatial reasoning abilities remain largely constrained to the observed images and text-or…
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…
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…
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…
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…