57 citations · 115 across the 7 of their papers we have counts for
8 papers
MeshQuery: Agentic Seam Planning for UV Parametrization
Marco Schouten, Arthur Roullier, Elie Michel +3
We present MeshQuery, a training-free agentic approach to automatic UV unwrapping of production-grade quad meshes. A Vision-Language Model (VLM) plans artist-aligned seams using a…
Single-Line Drawing Generation via Semantics-Driven Optimization
Tanguy Magne, Alexandre Binninger, Ruben Wiersma +1
Line drawings are a highly expressive art form that requires the artist to abstract and distill the essence of their subject. We present the first semantics-driven method for autom…
TetWeave: Isosurface Extraction using On-The-Fly Delaunay Tetrahedral Grids for Gradient-Based Mesh Optimization
Alexandre Binninger, Ruben Wiersma, Philipp Herholz +1
We introduce TetWeave, a novel isosurface representation for gradient-based mesh optimization that jointly optimizes the placement of a tetrahedral grid used for Marching Tetrahedr…
Uncertainty for SVBRDF Acquisition using Frequency Analysis
Ruben Wiersma, Julien Philip, Miloš Hašan +4
This paper aims to quantify uncertainty for SVBRDF acquisition in multi-view captures. Under uncontrolled illumination and unstructured viewpoints, there is no guarantee that the o…
A Fast Geometric Multigrid Method for Curved Surfaces
Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann +1
We introduce a geometric multigrid method for solving linear systems arising from variational problems on surfaces in geometry processing, Gravo MG. Our scheme uses point clouds as…
Deep vanishing point detection: Geometric priors make dataset variations vanish
Yancong Lin, Ruben Wiersma, Silvia L. Pintea +3
Deep learning has improved vanishing point detection in images. Yet, deep networks require expensive annotated datasets trained on costly hardware and do not generalize to even sli…
DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds
Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann +1
Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep learning on images and the increased availability of 3D~data. In this paper, we aim…
CNNs on Surfaces using Rotation-Equivariant Features
Ruben Wiersma, Elmar Eisemann, Klaus Hildebrandt
This paper is concerned with a fundamental problem in geometric deep learning that arises in the construction of convolutional neural networks on surfaces. Due to curvature, the tr…