PoNQ: a Neural QEM-based Mesh Representation

• URL: http://arxiv.org/abs/2403.12870v1
• Date: Tue, 19 Mar 2024 16:15:08 GMT
• Title: PoNQ: a Neural QEM-based Mesh Representation
• Authors: Nissim Maruani, Maks Ovsjanikov, Pierre Alliez, Mathieu Desbrun,
• Abstract summary: We introduce a learnable mesh representation through a set of local 3D sample Points and their associated Normals and Quadric error metrics (QEM) A global mesh is directly derived from PoNQ by efficiently leveraging the knowledge of the local quadric errors. We demonstrate the efficacy of PoNQ through a learning-based mesh prediction from SDF grids.
• Score: 33.81124790808585
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Although polygon meshes have been a standard representation in geometry processing, their irregular and combinatorial nature hinders their suitability for learning-based applications. In this work, we introduce a novel learnable mesh representation through a set of local 3D sample Points and their associated Normals and Quadric error metrics (QEM) w.r.t. the underlying shape, which we denote PoNQ. A global mesh is directly derived from PoNQ by efficiently leveraging the knowledge of the local quadric errors. Besides marking the first use of QEM within a neural shape representation, our contribution guarantees both topological and geometrical properties by ensuring that a PoNQ mesh does not self-intersect and is always the boundary of a volume. Notably, our representation does not rely on a regular grid, is supervised directly by the target surface alone, and also handles open surfaces with boundaries and/or sharp features. We demonstrate the efficacy of PoNQ through a learning-based mesh prediction from SDF grids and show that our method surpasses recent state-of-the-art techniques in terms of both surface and edge-based metrics.

Related papers

• SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes [61.110517195874074]
  We present a scheme to directly generate manifold, polygonal meshes of complex connectivity as the output of a neural network. Our key innovation is to define a continuous latent connectivity space at each mesh, which implies the discrete mesh. In applications, this approach not only yields high-quality outputs from generative models, but also enables directly learning challenging geometry processing tasks such as mesh repair.
  arXiv  Detail & Related papers  (2024-09-30T17:59:03Z)
• NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function [55.86697795177619]
  Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. We introduce a new paradigm for learning neural gradient functions, which encourages the neural network to fit the input point clouds. Our excellent results on widely used benchmarks demonstrate that our method can learn more accurate normals for both unoriented and oriented normal estimation tasks.
  arXiv  Detail & Related papers  (2023-11-01T09:25:29Z)
• SieveNet: Selecting Point-Based Features for Mesh Networks [41.74190660234404]
  Meshes are widely used in 3D computer vision and graphics, but their irregular topology poses challenges in applying them to existing neural network architectures. Recent advances in mesh neural networks turn to remeshing and push the boundary of pioneer methods that solely take the raw meshes as input. We propose SieveNet, a novel paradigm that takes into account both the regular topology and the exact geometry.
  arXiv  Detail & Related papers  (2023-08-24T03:40:16Z)
• Learning Self-Prior for Mesh Inpainting Using Self-Supervised Graph Convolutional Networks [4.424836140281846]
  We present a self-prior-based mesh inpainting framework that requires only an incomplete mesh as input. Our method maintains the polygonal mesh format throughout the inpainting process. We demonstrate that our method outperforms traditional dataset-independent approaches.
  arXiv  Detail & Related papers  (2023-05-01T02:51:38Z)
• NeuralMeshing: Differentiable Meshing of Implicit Neural Representations [63.18340058854517]
  We propose a novel differentiable meshing algorithm for extracting surface meshes from neural implicit representations. Our method produces meshes with regular tessellation patterns and fewer triangle faces compared to existing methods.
  arXiv  Detail & Related papers  (2022-10-05T16:52:25Z)
• Neural Marching Cubes [14.314650721573743]
  We introduce Neural Marching Cubes (NMC), a data-driven approach for extracting a triangle mesh from a discretized implicit field. We show that our network learns local features with limited fields, hence it generalizes well to new shapes and new datasets.
  arXiv  Detail & Related papers  (2021-06-21T17:18:52Z)
• Primal-Dual Mesh Convolutional Neural Networks [62.165239866312334]
  We propose a primal-dual framework drawn from the graph-neural-network literature to triangle meshes. Our method takes features for both edges and faces of a 3D mesh as input and dynamically aggregates them. We provide theoretical insights of our approach using tools from the mesh-simplification literature.
  arXiv  Detail & Related papers  (2020-10-23T14:49:02Z)
• A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries [62.28265459308354]
  Network learns end-to-end mapping between spatial positions and CFD quantities. Incompress laminar steady flow past a cylinder with various shapes for its cross section is considered. Network predicts the flow fields hundreds of times faster than our conventional CFD.
  arXiv  Detail & Related papers  (2020-10-15T12:15:02Z)
• Neural Subdivision [58.97214948753937]
  This paper introduces Neural Subdivision, a novel framework for data-driven coarseto-fine geometry modeling. We optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. We demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
  arXiv  Detail & Related papers  (2020-05-04T20:03:21Z)

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.