60 citations · 71 across the 49 of their papers we have counts for
14 papers · 1 filter
VLNVerse: A Benchmark for Vision-Language Navigation with Versatile, Embodied, Realistic Simulation and Evaluation
Sihao Lin, Zerui Li, Xunyi Zhao +10
Despite remarkable progress in Vision-Language Navigation (VLN), existing benchmarks remain confined to fixed, small-scale datasets with naive physical simulation. These shortcomin…
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…
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…
EgoExoBench: A Benchmark for First- and Third-person View Video Understanding in MLLMs
Yuping He, Yifei Huang, Guo Chen +4
Transferring and integrating knowledge across first-person (egocentric) and third-person (exocentric) viewpoints is intrinsic to human intelligence, enabling humans to learn from o…
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…
GLEAM: Learning Generalizable Exploration Policy for Active Mapping in Complex 3D Indoor Scenes
Xiao Chen, Tai Wang, Quanyi Li +3
Generalizable active mapping in complex unknown environments remains a critical challenge for mobile robots. Existing methods, constrained by insufficient training data and conserv…
A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
Xin Wen, Bingchen Zhao, Yilun Chen +2
Pre-trained vision models (PVMs) are fundamental to modern robotics, yet their optimal configuration remains unclear. Through systematic evaluation, we find that while DINO and iBO…
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…
VLM-Grounder: A VLM Agent for Zero-Shot 3D Visual Grounding
Runsen Xu, Zhiwei Huang, Tai Wang +3
3D visual grounding is crucial for robots, requiring integration of natural language and 3D scene understanding. Traditional methods depending on supervised learning with 3D point…
MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations
Ruiyuan Lyu, Jingli Lin, Tai Wang +8
With the emergence of LLMs and their integration with other data modalities, multi-modal 3D perception attracts more attention due to its connectivity to the physical world and mak…
What Makes CLIP More Robust to Long-Tailed Pre-Training Data? A Controlled Study for Transferable Insights
Xin Wen, Bingchen Zhao, Yilun Chen +2
Severe data imbalance naturally exists among web-scale vision-language datasets. Despite this, we find CLIP pre-trained thereupon exhibits notable robustness to the data imbalance…
Grounded 3D-LLM with Referent Tokens
Yilun Chen, Shuai Yang, Haifeng Huang +5
Prior studies on 3D scene understanding have primarily developed specialized models for specific tasks or required task-specific fine-tuning. In this study, we propose Grounded 3D-…
3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting
Xiaoyang Lyu, Yang-Tian Sun, Yi-Hua Huang +5
In this paper, we present an implicit surface reconstruction method with 3D Gaussian Splatting (3DGS), namely 3DGSR, that allows for accurate 3D reconstruction with intricate detai…
GenNBV: Generalizable Next-Best-View Policy for Active 3D Reconstruction
Xiao Chen, Quanyi Li, Tai Wang +2
While recent advances in neural radiance field enable realistic digitization for large-scale scenes, the image-capturing process is still time-consuming and labor-intensive. Previo…