4 citations · 4 across the 9 of their papers we have counts for
10 papers
Non-Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunities
Aleksei Zhuravlev, Lennart Bastian, Dongliang Cao +12
Estimating correspondences between deformed shape instances is a long-standing problem in computer graphics; numerous applications, from texture transfer to statistical modelling,…
Compensating connectivity restrictions in quantum annealers via splitting and linearization techniques
Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik +2
Current quantum annealing experiments often suffer from restrictions in connectivity in the sense that only certain qubits can be coupled to each other. The most common strategy to…
Beyond Complete Shapes: A Benchmark for Quantitative Evaluation of 3D Shape Surface Matching Algorithms
Viktoria Ehm, Nafie El Amrani, Yizheng Xie +9
Finding correspondences between 3D deformable shapes is an important and long-standing problem in geometry processing, computer vision, graphics, and beyond. While various shape ma…
SShaDe: scalable shape deformation via local representations
Filippo Maggioli, Daniele Baieri, Zorah Lähner +1
With the increase in computational power for the available hardware, the demand for high-resolution data in computer graphics applications increases. Consequently, classical geomet…
Implicit-ARAP: Efficient Handle-Guided Neural Field Deformation via Local Patch Meshing
Daniele Baieri, Filippo Maggioli, Emanuele Rodolà +2
Neural fields have emerged as a powerful representation for 3D geometry, enabling compact and continuous modeling of complex shapes. Despite their expressive power, manipulating ne…
Kissing to Find a Match: Efficient Low-Rank Permutation Representation
Hannah Dröge, Zorah Lähner, Yuval Bahat +3
Permutation matrices play a key role in matching and assignment problems across the fields, especially in computer vision and robotics. However, memory for explicitly representing…
A Simple Strategy to Provable Invariance via Orbit Mapping
Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner +2
Many applications require robustness, or ideally invariance, of neural networks to certain transformations of input data. Most commonly, this requirement is addressed by training d…
Intrinsic Neural Fields: Learning Functions on Manifolds
Lukas Koestler, Daniel Grittner, Michael Moeller +2
Neural fields have gained significant attention in the computer vision community due to their excellent performance in novel view synthesis, geometry reconstruction, and generative…
Training or Architecture? How to Incorporate Invariance in Neural Networks
Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner +2
Many applications require the robustness, or ideally the invariance, of a neural network to certain transformations of input data. Most commonly, this requirement is addressed by e…
Efficient Globally Optimal 2D-to-3D Deformable Shape Matching
Zorah Lähner, Emanuele Rodolà, Frank R. Schmidt +2
We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D shape represented as a planar curve and a 3D shape represented as a surface; the outpu…