9 papers
BendTwin: Robust Dense-to-Sparse Physical Reconstruction with Bending-Aware Differentiable Spring-Mass Models
Yixiong Jing, Qi Wang, Lin Chen +6
Reconstructing objects with mechanical properties from video observations enables physically consistent dynamic prediction, benefiting robotics planning and interaction. Existing s…
UnderOneFacade: Worldwide Facade Semantic Segmentation Benchmark Dataset
Yi Wang, Fan Wang, Prabin Gyawali +9
Globally consistent semantic digital twins require centimeter-accurate and geographically transferable 3D facade segmentation. However, progress in facade parsing is limited by the…
You Only Touch Once: 6-DoF Object Pose Estimation from Single Tactile Contact
Pengfei Ye, Yuxiang Ma, Haonan Chen +5
Accurate 6-DoF object pose estimation is fundamental to robotic manipulation, yet vision-based methods often fail under occlusion, poor lighting, and reflective or transparent surf…
NEXUS: Neural Energy Fields for Physically Consistent Contact-Rich 3D Object Dynamics
Qizhen Ying, Guangming Wang, Yangchen Pan +3
Physics-grounded video generation requires controllable 3D object dynamics that remain physically consistent under contact, deformation, and external forcing. Existing trajectory-b…
RoboFlow4D: A Lightweight Flow World Model Toward Real-Time Flow-Guided Robotic Manipulation
Sixu Lin, Junliang Chen, Huaiyuan Xu +8
Planning and acting in 3D environments is a fundamental capability for robotic manipulation in the real world. Although prior work has explored predictive flow planners to guide 3D…
PhySPRING: Structure-Preserving Reduction of Physics-Informed Twins via GNN
Yixiong Jing, Xingyuan Chen, Guangming Wang +3
Physics-based digital twins aim to predict the dynamics of real-world objects under interaction, enabling real-to-sim-to-real applications in robotics. Current approaches reconstru…