- publication
- ACM Transactions on Graphics, presented at SIGGRAPH 2025
- authors
- Alexandre Binninger, Ruben Wiersma, Philipp Herholz, Olga Sorkine-Hornung
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.
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acknowledgments
We thank the anonymous reviewers for their constructive feedback and Marcel Padilla for his careful reading of our manuscript. The open source codebases of FlexiCubes and DMTet have been instrumental in the development of TetWeave. This work was supported in part by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 101003104, ERC CoG MYCLOTH).