TetWeave: Isosurface Extraction using On-The-Fly Delaunay Tetrahedral Grids for Gradient-Based Mesh Optimization

• URL: http://arxiv.org/abs/2505.04590v2
• Date: Thu, 08 May 2025 08:24:30 GMT
• Title: TetWeave: Isosurface Extraction using On-The-Fly Delaunay Tetrahedral Grids for Gradient-Based Mesh Optimization
• Authors: Alexandre Binninger, Ruben Wiersma, Philipp Herholz, Olga Sorkine-Hornung,
• Abstract summary: We introduce TetWeave, a novel isosurface representation for gradient-based mesh optimization.<n>TetWeave constructs tetrahedral grids on-the-fly via Delaunay triangulation.<n>We demonstrate the applicability of TetWeave to a broad range of challenging tasks in computer graphics and vision.
• Score: 59.318328774645835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce TetWeave, a novel isosurface representation for gradient-based mesh optimization that jointly optimizes the placement of a tetrahedral grid used for Marching Tetrahedra and a novel directional signed distance at each point. TetWeave constructs tetrahedral grids on-the-fly via Delaunay triangulation, enabling increased flexibility compared to predefined grids. The extracted meshes are guaranteed to be watertight, two-manifold and intersection-free. The flexibility of TetWeave enables a resampling strategy that places new points where reconstruction error is high and allows to encourage mesh fairness without compromising on reconstruction error. This leads to high-quality, adaptive meshes that require minimal memory usage and few parameters to optimize. Consequently, TetWeave exhibits near-linear memory scaling relative to the vertex count of the output mesh - a substantial improvement over predefined grids. We demonstrate the applicability of TetWeave to a broad range of challenging tasks in computer graphics and vision, such as multi-view 3D reconstruction, mesh compression and geometric texture generation.

Related papers

• MILo: Mesh-In-the-Loop Gaussian Splatting for Detailed and Efficient Surface Reconstruction [28.452920446301608]
  We present MILo, a novel framework that bridges the gap between volumetric and surface representations by differentiably extracting a mesh from the 3D Gaussians.<n>Our approach can reconstruct complete scenes, including backgrounds, with state-of-the-art quality while requiring an order of magnitude fewer mesh vertices than previous methods.
  arXiv  Detail & Related papers  (2025-06-30T17:48:54Z)
• FlexPara: Flexible Neural Surface Parameterization [71.65203972602673]
  This paper introduces FlexPara, an unsupervised neural optimization framework to achieve both global and multi-chart surface parameterizations.<n>We ingeniously design and combine a series of geometrically-interpretable sub-networks, with specific functionalities, to construct a bi-directional cycle mapping framework for global parameterization.<n>Experiments demonstrate the universality, superiority, and inspiring potential of our neural surface parameterization paradigm.
  arXiv  Detail & Related papers  (2025-04-27T12:30:08Z)
• GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction [79.42244344704154]
  GausSurf employs geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene.<n>Our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.
  arXiv  Detail & Related papers  (2024-11-29T03:54:54Z)
• Tetrahedron Splatting for 3D Generation [39.24591650300784]
  Tetrahedron Splatting (TeT-Splatting) supports easy convergence during optimization, precise mesh extraction, and real-time rendering simultaneously. Our representation can be trained without mesh extraction, making the optimization process easier to converge.
  arXiv  Detail & Related papers  (2024-06-03T17:56:36Z)
• TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes [47.47768820192874]
  TetSphere splatting represents 3D shapes by deforming a collection of tetrahedral spheres.<n>It addresses common mesh issues such as irregular triangles, non-manifoldness, and floating artifacts.<n>It seamlessly integrates into generative modeling tasks, such as image-to-3D and text-to-3D generation.
  arXiv  Detail & Related papers  (2024-05-30T17:35:49Z)
• HVOFusion: Incremental Mesh Reconstruction Using Hybrid Voxel Octree [12.180935725861723]
  We propose a novel hybrid voxel-octree approach to fuse octree with voxel structures. Such sparse structure preserves triangular faces in the leaf nodes and produces partial meshes sequentially for incremental reconstruction. Experimental results on several datasets show that our proposed approach is capable of quickly and accurately reconstructing a scene with realistic colors.
  arXiv  Detail & Related papers  (2024-04-27T18:24:53Z)
• Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis [70.40950409274312]
  We modify density fields to encourage them to converge towards surfaces, without compromising their ability to reconstruct thin structures. We also develop a fusion-based meshing strategy followed by mesh simplification and appearance model fitting. The compact meshes produced by our model can be rendered in real-time on mobile devices.
  arXiv  Detail & Related papers  (2024-02-19T18:59:41Z)
• GridFormer: Point-Grid Transformer for Surface Reconstruction [37.30776475324579]
  We introduce a novel attention mechanism between the grid and point features named Point-Grid Transformer (GridFormer) This mechanism treats the grid as a transfer point connecting the space and point cloud. We also propose a boundary optimization strategy incorporating margin binary cross-entropy loss and boundary sampling.
  arXiv  Detail & Related papers  (2024-01-04T14:31:56Z)
• Flexible Isosurface Extraction for Gradient-Based Mesh Optimization [65.76362454554754]
  This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives.
  arXiv  Detail & Related papers  (2023-08-10T06:40:19Z)
• Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement [78.48648360358193]
  We present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency appearance with a NeRF. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering.
  arXiv  Detail & Related papers  (2023-03-03T17:14:44Z)

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.