HoloTetSphere: Unified TetSphere Mesh Reconstruction for Physical Simulations
- URL: http://arxiv.org/abs/2607.08398v1
- Date: Thu, 09 Jul 2026 12:21:43 GMT
- Title: HoloTetSphere: Unified TetSphere Mesh Reconstruction for Physical Simulations
- Abstract summary: We propose a topology-adaptive framework for holistic tetrahedral mesh reconstruction through end-to-end topological and geometric optimization.<n>Our method outperforms state-of-the-art techniques by achieving superior geometric accuracy and producing coherent, single-connected tetrahedral meshes.
- Score: 16.69225871436957
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Standard pipelines for physics-ready 3D reconstruction rely on a decoupled two-stage paradigm: extracting surface geometry followed by an error-prone tetrahedralization process. While recent Lagrangian methods like TetSphere Splatting attempt to bypass this by directly optimizing volumetric primitives, their homeomorphic constraints prevent topology-adaptive optimization. Consequently, they produce disjoint tetrahedra rather than a single connected mesh, rendering the structures unsuitable for further physical simulations. To address this, we propose a topology-adaptive framework for holistic tetrahedral mesh reconstruction through end-to-end topological and geometric optimization. First, by coupling Gaussian spheres to tetrahedral elements and leveraging edge connections, we estimate a continuous opacity field for differentiable element pruning. Next, jointly minimizing mesh smoothing energy and multi-view Gaussian rendering error drives alternating geometric refinement while preserving topological adaptivity. Consequently, our approach effectively constructs a unified and topologically coherent tetrahedral mesh. Extensive experiments demonstrate that our method outperforms state-of-the-art techniques by achieving superior geometric accuracy and producing coherent, single-connected tetrahedral meshes, thereby effectively bypassing the error-prone conventional tetrahedralization step for reconstructed surface meshes and streamlining downstream physical simulation.
Related papers
- P2Voxel: Pyramid Pivot Voxelization for 3D Mesh Tokenization [60.385793205570046]
We introduce textbfP2Voxel, a pyramid pivot voxelization framework for compact and reconstruction-aware mesh tokenization.<n>Under the textitSpatial Complexity assumption, Pyramid Pivot Voxelization exploits the spatial non-uniformity of real surfaces.<n>Under the textitBlock Reconstructability assumption, a Pyramid VAE learns compact multi-resolution latent codes over locally reconstructable pivot blocks.
arXiv Detail & Related papers (2026-08-01T13:04:29Z) - RODR: Riemannian Orthogonally Decoupled Regularization for Disentangled Manifold Representation [0.91963810502252]
We introduce a generic and interpretable framework for disentangled geometric optimization in point cloud processing.<n> Experiments demonstrate that RODR reaches performance comparable to state-of-the-art baselines.
arXiv Detail & Related papers (2026-07-27T03:12:14Z) - TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields [72.76537377865192]
TriFlow is a new generative approach for producing compact 3D meshes with artist-like triangle topology directly from input geometry conditions such as signed distance fields.<n>Experiments demonstrate that TriFlow achieves stronger generalization and significantly improved topology quality compared to state-of-the-art learning-based approaches.
arXiv Detail & Related papers (2026-06-18T11:55:00Z) - Inverse Rendering for High-Genus 3D Surface Meshes from Multi-view Images with Persistent Homology Priors [11.227213428407673]
Reconstructing 3D objects from images is inherently an ill-posed problem due to ambiguities in geometry, appearance, and topology.<n>This paper introduces collaborative rendering with persistent homology priors, a novel strategy that leverages topological constraints to resolve these ambiguities.
arXiv Detail & Related papers (2026-01-17T20:06:19Z) - The N-5 Scaling Law: Topological Dimensionality Reduction in the Optimal Design of Fully-actuated Multirotors [3.919116384604024]
We formulate the design problem on the product manifold of Projective Lines RP2N.<n>We show that the topology of the global optima is governed strictly by the symmetry of the chassis.
arXiv Detail & Related papers (2025-12-29T17:25:41Z) - TetWeave: Isosurface Extraction using On-The-Fly Delaunay Tetrahedral Grids for Gradient-Based Mesh Optimization [59.318328774645835]
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.
arXiv Detail & Related papers (2025-05-07T17:32:49Z) - STITCH: Surface reconstrucTion using Implicit neural representations with Topology Constraints and persistent Homology [23.70495314317551]
We present STITCH, a novel approach for neural implicit surface reconstruction of a sparse and irregularly spaced point cloud.<n>We develop a new differentiable framework based on persistent homology to formulate topological loss terms that enforce the prior of a single 2-manifold object.
arXiv Detail & Related papers (2024-12-24T22:55:35Z) - AniSDF: Fused-Granularity Neural Surfaces with Anisotropic Encoding for High-Fidelity 3D Reconstruction [55.69271635843385]
We present AniSDF, a novel approach that learns fused-granularity neural surfaces with physics-based encoding for high-fidelity 3D reconstruction.<n>Our method boosts the quality of SDF-based methods by a great scale in both geometry reconstruction and novel-view synthesis.
arXiv Detail & Related papers (2024-10-02T03:10:38Z) - 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) - 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) - 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)
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.