arXiv Machine Learning

Robust Biharmonic Skinning Using Geometric Fields

arXiv:2406. 00238v3 Announce Type: replace-cross Abstract: Bounded bihramonic weights are a popular tool used to rig and deform characters for animation, to compute reduced-order simulations, and to define feature descriptors for geometry processing.

arXiv Computer Vision
Sep 1

Elastic Triangle Splatting

arXiv:2608.29106v1 Announce Type: new Abstract: While neural rendering methods such as 3D Gaussian Splatting achieve remarkable visual fidelity, traditional polygonal meshes remain the backbone of es...

By Tian Shi, Shenhan Qian, Daniel Cremers
arXiv Computer Vision
Aug 25

Differentiable Voxelization of Surface Representations

The paper introduces a differentiable voxelization technique that computes gradients of volumetric properties, such as winding numbers, with respect to surface mesh parameters. This method allows efficient optimization of triangle meshes using voxel-based volume samples on a regular grid. The authors demonstrate its utility in applications like resolving mesh intersections, designing manufacturable shapes for bandsaw cutting, and creating near-tiling 3D structures.

By Tobias Djuren, Ugo Finnendahl, Markus Worchel, Hendrik Meyer, Marc Alexa
arXiv Computer Vision
Sep 2

TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields

TriFlow introduces a generative method for creating compact 3D meshes with artist‑like triangle topology directly from input geometry such as signed distance fields. It represents mesh topology as a nearest‑vertex vector field (NVF) over the surface, trains a latent flow‑matching model to synthesize this field, and then clusters surface regions to guide a constrained quadric error metric simplification. The resulting meshes closely match the input geometry while exhibiting structured, artist‑like connectivity, achieving 90% lower Chamfer Distance and an 8× speedup over state‑of‑the‑art learning‑based approaches.

By Haoxuan Li, Ziya Erko\c{c}, Daniele Sirigatti, Vladislav Rosov, Lei Li, Angela Dai, Matthias Nie{\ss}ner
arXiv Computer Vision
Sep 25

ToCo-Mesh: Topology-Consistent Dynamic Mesh Reconstruction via Adaptive Tessellation and Surface-Aligned 2DGS

ToCo-Mesh is a dynamic mesh reconstruction framework that preserves topology consistency while achieving high‑fidelity geometry from multi‑view temporal images. It uses a dual‑mesh representation, coupling a canonical template mesh to time‑varying coarse guide meshes via barycentric parameterization, and applies error‑driven split‑and‑merge operations on the template to refine detail. A Surface‑Aligned 2DGS module anchors flattened Gaussians to mesh faces, using rendered normals to guide fine‑tuning and suppress surface irregularities, resulting in photorealistic rendering.

By Chuanjin Fan, Wenjie Chang, Aibing Li, Bingzhou Wang, Wenfei Yang, Tianzhu Zhang
arXiv AI
Jul 28

DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces

arXiv:2603. 05607v2 Announce Type: replace-cross Abstract: Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels.

By Mohammad Sadil Khan, Muhammad Usama, Rolandos Alexandros Potamias, Didier Stricker, Muhammad Zeshan Afzal, Jiankang Deng, Ismail Elezi
arXiv Machine Learning
Jul 28

Meshless Domain Randomization via Explicit Parameter Perturbation of 3D Gaussian Splatting

arXiv:2607. 22890v1 Announce Type: cross Abstract: Domain Randomization (DR) is a standard technique for closing the Sim-to-Real gap, yet traditional DR pipelines rely on classical computer graphics rendering driven by polygon meshes.

By Felipe Nunes Carbone de Carvalho, Joyce de Morais Souza, Alan de Aguiar, Charles Morphy D. Santos, Jo\~ao Paulo Gois
arXiv Machine Learning
Jun 3

CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization

arXiv:2605. 01171v2 Announce Type: replace-cross Abstract: Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets.

By Ghadi Nehme, Eamon Whalen, Faez Ahmed