activity
20232026
most citedPointLLM: Empowering Large Language Models to Understand Point Clouds

5 citations · 16 across the 21 of their papers we have counts for

collaborators
Showing 2025Show all

10 papers · 1 filter

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

ChangingGrounding: 3D Visual Grounding in Changing Scenes

Miao Hu, Zhiwei Huang, Tai Wang +4

Real-world robots localize objects from natural-language instructions while scenes around them keep changing. Yet most of the existing 3D visual grounding (3DVG) method still assum…

cs.CV2025

VFlowOpt: A Token Pruning Framework for LMMs with Visual Information Flow-Guided Optimization

Sihan Yang, Runsen Xu, Chenhang Cui +3

Large Multimodal Models (LMMs) excel in visual-language tasks by leveraging numerous visual tokens for fine-grained visual information, but this token redundancy results in signifi…

cs.RO2025

Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities

Liuyi Wang, Xinyuan Xia, Hui Zhao +6

Recent Vision-and-Language Navigation (VLN) advancements are promising, but their idealized assumptions about robot movement and control fail to reflect physically embodied deploym…

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.RO2025

StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling

Meng Wei, Chenyang Wan, Xiqian Yu +9

Vision-and-Language Navigation (VLN) in real-world settings requires agents to process continuous visual streams and generate actions with low latency grounded in language instruct…

cs.RO2025

CronusVLA: Towards Efficient and Robust Manipulation via Multi-Frame Vision-Language-Action Modeling

Hao Li, Shuai Yang, Yilun Chen +8

Recent vision-language-action (VLA) models built on pretrained vision-language models (VLMs) have demonstrated strong performance in robotic manipulation. However, these models rem…

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…

cs.CV2025★ 1 cited

Language-to-Space Programming for Training-Free 3D Visual Grounding

Boyu Mi, Hanqing Wang, Tai Wang +2

3D visual grounding (3DVG) is challenging due to the need to understand 3D spatial relations. While supervised approaches have achieved superior performance, they are constrained b…