Abstract Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize
images of 3D scenes from novel views. However, they rely upon specialized volumetric …

Flexible Isosurface Extraction for Gradient- Based Mesh Optimization Page 1 Flexible Isosurface
Extraction for GradientBased Mesh Optimization Tianchang Shen, Jacob Munkberg, Jon …

Recent progress in neural implicit functions has set new state-of-the-art in reconstructing
high-fidelity 3D shapes from a collection of images. However, these approaches are limited …

Mesh generation is of great value in various applications involving computer graphics and
virtual content, yet designing generative models for meshes is challenging due to their …

Intelligent Mesh Generation (IMG) represents a novel and promising field of research,
utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG …

In stark contrast to the case of images, finding a concise, learnable discrete representation of
3D surfaces remains a challenge. In particular, while polygon meshes are arguably the most …

Extracting parametric edge curves from point clouds is a fundamental problem in 3D vision
and geometry processing. Existing approaches mainly rely on keypoint detection, a …

We propose a method, named DualMesh-UDF, to extract a surface from unsigned distance
functions (UDFs), encoded by neural networks, or neural UDFs. Neural UDFs are becoming …

In this paper, we propose a new method, called DoubleCoverUDF, for extracting the zero
level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and …

We propose a robust re-meshing approach that can automatically generate visual-
preserving low-poly meshes for any high-poly models found in the wild. Our method can be …