arXiv Machine Learning
Aug 12

HyperShape: Hyperelasticity Across Diverse Shapes

arXiv:2608. 09938v1 Announce Type: cross Abstract: Hyperelastic deformations are highly sensitive to domain geometry and boundary conditions, making generalization across both a critical capability for neural operators applied to these problems.

By Leo Widmer, Sidaty El Hadramy, St\'ephane Cotin, Philippe Claude Cattin
arXiv Computer Vision
Aug 26

Voronoi-Assisted Optimization for Diffusing Unsigned Distance Fields from Unoriented Points

The paper introduces VAD, a lightweight, network‑free method for computing Unsigned Distance Fields (UDFs) from unoriented point clouds. It assigns bi‑directional normals using two Voronoi‑based criteria, diffuses these normals to approximate a UDF gradient field, and then integrates to recover the final UDF. Experiments show VAD handles watertight, open, non‑manifold, and non‑orientable geometries efficiently and stably.

By Jiayi Kong, Chen Zong, Junkai Deng, Xuhui Chen, Fei Hou, Shiqing Xin, Junhui Hou, Chen Qian, Ying He
Hugging Face Trending Papers
Jul 21

Fluid-SDF: Ultra-Lightweight and Editable Implicit Shape Representation via Differentiable Primitives

Implicit Neural Representations (INRs) have become the standard for continuous 2D shape modeling, but they suffer from black-box uneditability, vulnerability to noise, and high parameter counts that severely hinder deployment on edge devices. We introduce Fluid-SDF, a highly compressed, differentiable Constructive Solid Geometry (CSG) framework that models shapes using explicit geometric primitives blended via a smooth minimum function.