High Resolution UDF Meshing via Iterative Networks

• URL: http://arxiv.org/abs/2509.17212v1
• Date: Sun, 21 Sep 2025 19:39:54 GMT
• Title: High Resolution UDF Meshing via Iterative Networks
• Authors: Federico Stella, Nicolas Talabot, Hieu Le, Pascal Fua,
• Abstract summary: Unsigned Distance Fields (UDFs) are a natural implicit representation for open surfaces but, unlike Signed Distance Fields (SDFs), are challenging to triangulate into explicit meshes.<n>This is especially true at high resolutions where neural UDFs exhibit higher noise levels, which makes it hard to capture fine details.<n>We show that this can be remedied by performing several passes and by reasoning on previously extracted surface elements to incorporate neighborhood information.<n>Our key contribution is an iterative neural network that does this and progressively improves surface recovery within each voxel by spatially propagating information from increasingly distant neighbors.
• Score: 35.20698867678239
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsigned Distance Fields (UDFs) are a natural implicit representation for open surfaces but, unlike Signed Distance Fields (SDFs), are challenging to triangulate into explicit meshes. This is especially true at high resolutions where neural UDFs exhibit higher noise levels, which makes it hard to capture fine details. Most current techniques perform within single voxels without reference to their neighborhood, resulting in missing surface and holes where the UDF is ambiguous or noisy. We show that this can be remedied by performing several passes and by reasoning on previously extracted surface elements to incorporate neighborhood information. Our key contribution is an iterative neural network that does this and progressively improves surface recovery within each voxel by spatially propagating information from increasingly distant neighbors. Unlike single-pass methods, our approach integrates newly detected surfaces, distance values, and gradients across multiple iterations, effectively correcting errors and stabilizing extraction in challenging regions. Experiments on diverse 3D models demonstrate that our method produces significantly more accurate and complete meshes than existing approaches, particularly for complex geometries, enabling UDF surface extraction at higher resolutions where traditional methods fail.

Related papers

• Neural Surface Detection for Unsigned Distance Fields [51.77214039267625]
  We introduce a deep-learning approach to taking a UDF and turning it locally into an SDF, so that it can be effectively triangulated using existing algorithms. We show that it achieves better accuracy in surface detection than existing methods. We also demonstrate the flexibily of the method by using it in conjunction with DualMeshUDF, a state of the art dual meshing method that can operate on UDFs.
  arXiv  Detail & Related papers  (2024-07-25T20:31:40Z)
• Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction [26.35134181204119]
  We propose Details Enhanced UDF (DEUDF) learning that integrates normal alignment and the SIREN network.<n>Our computational results demonstrate that DEUDF outperforms existing UDF learning methods in both accuracy and the quality of reconstructed surfaces.
  arXiv  Detail & Related papers  (2024-06-01T07:52:26Z)
• DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling [0.20287200280084108]
  We learn a hyperbolic scaling of the unsigned distance field, which defines a new Eikonal problem with distinct boundary conditions. Our approach not only addresses the challenge of open surface representation but also demonstrates significant improvement in reconstruction quality and training performance.
  arXiv  Detail & Related papers  (2024-02-14T00:42:19Z)
• NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering [25.078149064632218]
  NeUDF can reconstruct surfaces with arbitrary topologies solely from multi-view supervision. We extensively evaluate our method over a number of challenging datasets, including DTU, MGN, and Deep Fashion 3D.
  arXiv  Detail & Related papers  (2023-04-20T04:14:42Z)
• GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation [73.77505964222632]
  We present a learning-based method, namely GeoUDF, to tackle the problem of reconstructing a discrete surface from a sparse point cloud. To be specific, we propose a geometry-guided learning method for UDF and its gradient estimation. To extract triangle meshes from the predicted UDF, we propose a customized edge-based marching cube module.
  arXiv  Detail & Related papers  (2022-11-30T06:02:01Z)
• NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies [87.06532943371575]
  We present a novel method, called NeuralUDF, for reconstructing surfaces with arbitrary topologies from 2D images via volume rendering. In this paper, we propose to represent surfaces as the Unsigned Distance Function (UDF) and develop a new volume rendering scheme to learn the neural UDF representation.
  arXiv  Detail & Related papers  (2022-11-25T15:21:45Z)
• CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimization [54.69408516025872]
  CAP-UDF is a novel method to learn consistency-aware UDF from raw point clouds. We train a neural network to gradually infer the relationship between queries and the approximated surface. We also introduce a polygonization algorithm to extract surfaces using the gradients of the learned UDF.
  arXiv  Detail & Related papers  (2022-10-06T08:51:08Z)
• MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks [68.82901764109685]
  Recent work modelling 3D open surfaces train deep neural networks to approximate Unsigned Distance Fields (UDFs) We propose to directly mesh deep UDFs as open surfaces with an extension of marching cubes, by locally detecting surface crossings. Our method is order of magnitude faster than meshing a dense point cloud, and more accurate than inflating open surfaces.
  arXiv  Detail & Related papers  (2021-11-29T14:24:02Z)
• Deep Implicit Surface Point Prediction Networks [49.286550880464866]
  Deep neural representations of 3D shapes as implicit functions have been shown to produce high fidelity models. This paper presents a novel approach that models such surfaces using a new class of implicit representations called the closest surface-point (CSP) representation.
  arXiv  Detail & Related papers  (2021-06-10T14:31:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.