1 citations · 2 across the 5 of their papers we have counts for
7 papers
DGGT: Feedforward 4D Reconstruction of Dynamic Driving Scenes using Unposed Images
Xiaoxue Chen, Ziyi Xiong, Yuantao Chen +11
Autonomous driving needs fast, scalable 4D reconstruction and re-simulation for training and evaluation, yet most methods for dynamic driving scenes still rely on per-scene optimiz…
Unifying Appearance Codes and Bilateral Grids for Driving Scene Gaussian Splatting
Nan Wang, Yuantao Chen, Lixing Xiao +11
Neural rendering techniques, including NeRF and Gaussian Splatting (GS), rely on photometric consistency to produce high-quality reconstructions. However, in real-world scenarios,…
PUGS: Zero-shot Physical Understanding with Gaussian Splatting
Yinghao Shuai, Ran Yu, Yuantao Chen +9
Current robotic systems can understand the categories and poses of objects well. But understanding physical properties like mass, friction, and hardness, in the wild, remains chall…
UnitedVLN: Generalizable Gaussian Splatting for Continuous Vision-Language Navigation
Guangzhao Dai, Jian Zhao, Yuantao Chen +6
Vision-and-Language Navigation (VLN), where an agent follows instructions to reach a target destination, has recently seen significant advancements. In contrast to navigation in di…
Drone-assisted Road Gaussian Splatting with Cross-view Uncertainty
Saining Zhang, Baijun Ye, Xiaoxue Chen +5
Robust and realistic rendering for large-scale road scenes is essential in autonomous driving simulation. Recently, 3D Gaussian Splatting (3D-GS) has made groundbreaking progress i…
Blending Distributed NeRFs with Tri-stage Robust Pose Optimization
Baijun Ye, Caiyun Liu, Xiaoyu Ye +6
Due to the limited model capacity, leveraging distributed Neural Radiance Fields (NeRFs) for modeling extensive urban environments has become a necessity. However, current distribu…