1 citations · 3 across the 16 of their papers we have counts for
20 papers
Teaching DINOv3 About Partial 3D Geometry: A Self-Supervised Geometry-Aware Approach
Viktoria Ehm, Dongliang Cao, Riccardo Marin +4
Partial shape matching is a crucial yet underexplored problem in 3D vision, with significant relevance to real-world scenarios where shapes are often only partially observed. Exist…
Towards Stellarator Geometry Optimisation for Nuclear Fusion
Tobias Weißberg, Moritz Heep, Zorah Lähner +1
We present a local geometry refinement method and a 2D latent representation that took us to the top of the ConStellaration leaderboard on the geometric task in May 2026.
Unsupervised Pixel-Level Semantic Left-Right Understanding of In-the-Wild Images
Weikang Wang, Tobias Weißberg, Florian Bernard
While various works address reflective symmetry understanding in 3D data and images, pixel-level semantic left-right prediction of in-the-wild images remains challenging, due to ce…
Hyper-Network Neural Functional Maps for Unsupervised Robust 3D Shape Matching
Dongliang Cao, Florian Bernard
Functional maps are the cornerstone of recent non-rigid 3D shape matching methods due to their efficiency and performance. However, existing methods struggle with challenging scena…
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,…
An Integer Linear Programming Approach to Geometrically Consistent Partial-Partial Shape Matching
Viktoria Ehm, Paul Roetzer, Florian Bernard +1
The task of establishing correspondences between two 3D shapes is a long-standing challenge in computer vision. While numerous studies address full-full and partial-full 3D shape m…
Symmetry Informative and Agnostic Feature Disentanglement for 3D Shapes
Tobias Weißberg, Weikang Wang, Paul Roetzer +2
Shape descriptors, i.e., per-vertex features of 3D meshes or point clouds, are fundamental to shape analysis. Historically, various handcrafted geometry-aware descriptors and featu…
Hyper Diffusion Avatars: Dynamic Human Avatar Generation using Network Weight Space Diffusion
Dongliang Cao, Guoxing Sun, Marc Habermann +1
Creating human avatars is a highly desirable yet challenging task. Recent advancements in radiance field rendering have achieved unprecedented photorealism and real-time performanc…
Symmetry Understanding of 3D Shapes via Chirality Disentanglement
Weikang Wang, Tobias Weißberg, Nafie El Amrani +1
Chirality information (i.e. information that allows distinguishing left from right) is ubiquitous for various data modes in computer vision, including images, videos, point clouds,…
TwoSquared: 4D Generation from 2D Image Pairs
Lu Sang, Zehranaz Canfes, Dongliang Cao +3
Despite the astonishing progress in generative AI, 4D dynamic object generation remains an open challenge. With limited high-quality training data and heavy computing requirements,…
Fast Globally Optimal and Geometrically Consistent 3D Shape Matching
Paul Roetzer, Florian Bernard
Geometric consistency, i.e. the preservation of neighbourhoods, is a natural and strong prior in 3D shape matching. Geometrically consistent matchings are crucial for many downstre…
4Deform: Neural Surface Deformation for Robust Shape Interpolation
Lu Sang, Zehranaz Canfes, Dongliang Cao +3
Generating realistic intermediate shapes between non-rigidly deformed shapes is a challenging task in computer vision, especially with unstructured data (e.g., point clouds) where…
Implicit Neural Surface Deformation with Explicit Velocity Fields
Lu Sang, Zehranaz Canfes, Dongliang Cao +2
In this work, we introduce the first unsupervised method that simultaneously predicts time-varying neural implicit surfaces and deformations between pairs of point clouds. We propo…
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…
Synchronous Diffusion for Unsupervised Smooth Non-Rigid 3D Shape Matching
Dongliang Cao, Zorah Laehner, Florian Bernard
Most recent unsupervised non-rigid 3D shape matching methods are based on the functional map framework due to its efficiency and superior performance. Nevertheless, respective meth…
Partial-to-Partial Shape Matching with Geometric Consistency
Viktoria Ehm, Maolin Gao, Paul Roetzer +3
Finding correspondences between 3D shapes is an important and long-standing problem in computer vision, graphics and beyond. A prominent challenge are partial-to-partial shape matc…
Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation
Dongliang Cao, Marvin Eisenberger, Nafie El Amrani +2
Although 3D shape matching and interpolation are highly interrelated, they are often studied separately and applied sequentially to relate different 3D shapes, thus resulting in su…
Revisiting Map Relations for Unsupervised Non-Rigid Shape Matching
Dongliang Cao, Paul Roetzer, Florian Bernard
We propose a novel unsupervised learning approach for non-rigid 3D shape matching. Our approach improves upon recent state-of-the art deep functional map methods and can be applied…
DiscoMatch: Fast Discrete Optimisation for Geometrically Consistent 3D Shape Matching
Paul Roetzer, Ahmed Abbas, Dongliang Cao +2
In this work we propose to combine the advantages of learningbased and combinatorial formalisms for 3D shape matching. While learningbased methods lead to state-of-the-art matching…
Geometrically Consistent Partial Shape Matching
Viktoria Ehm, Paul Roetzer, Marvin Eisenberger +3
Finding correspondences between 3D shapes is a crucial problem in computer vision and graphics, which is for example relevant for tasks like shape interpolation, pose transfer, or…