80 citations · 85 across the 8 of their papers we have counts for
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…
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…
CT-NeRF: Incremental Optimizing Neural Radiance Field and Poses with Complex Trajectory
Yunlong Ran, Yanxu Li, Qi Ye +4
Neural radiance field (NeRF) has achieved impressive results in high-quality 3D scene reconstruction. However, NeRF heavily relies on precise camera poses. While recent works like…
Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration
Jing Zeng, Yanxu Li, Jiahao Sun +3
Implicit neural representations have demonstrated significant promise for 3D scene reconstruction. Recent works have extended their applications to autonomous implicit reconstructi…
Seal-3D: Interactive Pixel-Level Editing for Neural Radiance Fields
Xiangyu Wang, Jingsen Zhu, Qi Ye +4
With the popularity of implicit neural representations, or neural radiance fields (NeRF), there is a pressing need for editing methods to interact with the implicit 3D models for t…
Efficient View Path Planning for Autonomous Implicit Reconstruction
Jing Zeng, Yanxu Li, Yunlong Ran +6
Implicit neural representations have shown promising potential for the 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain…
NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations
Yunlong Ran, Jing Zeng, Shibo He +5
Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying t…