6 papers
Gradient Descent with Projection Finds Over-Parameterized Neural Networks for Learning Low-Degree Polynomials with Nearly Minimax Optimal Rate
Yingzhen Yang, Ping Li
We study the problem of learning a low-degree spherical polynomial of degree defined on the unit sphere in $\RR^d$ by training an over-parameterized two-layer ne…
SeeClear: Reliable Transparent Object Depth Estimation via Generative Opacification
Xiaoying Wang, Yumeng He, Jingkai Shi +4
Monocular depth estimation remains challenging for transparent objects, where refraction and transmission are difficult to model and break the appearance assumptions used by depth…
M-ABD: Scalable, Efficient, and Robust Multi-Affine-Body Dynamics
Zhiyong He, Dewen Guo, Minghao Guo +6
Simulating large-scale articulated assemblies poses a significant challenge due to the numerical stiffness and geometric complexity of jointed structures. Conventional rigid body s…
MPM Lite: Linear Kernels and Integration without Particles
Xiang Feng, Yunuo Chen, Chang Yu +6
In this paper, we introduce MPM Lite, a new hybrid Lagrangian/Eulerian method that eliminates the need for particle-based quadrature at solve time. Standard MPM practices suffer fr…
VoroLight: Learning Voronoi Surface Meshes via Sphere Intersection
Jiayin Lu, Ying Jiang, Yumeng He +2
Voronoi diagrams naturally produce convex, watertight, and topologically consistent cells, making them an appealing representation for 3D shape reconstruction. However, standard di…
Right-Side-Out: Learning Zero-Shot Sim-to-Real Garment Reversal
Chang Yu, Siyu Ma, Wenxin Du +9
Turning garments right-side out is a challenging manipulation task: it is highly dynamic, entails rapid contact changes, and is subject to severe visual occlusion. We introduce Rig…