42 citations · 45 across the 13 of their papers we have counts for
16 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…
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,…
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…
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,…
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…
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…
DefCor-Net: Physics-Aware Ultrasound Deformation Correction
Zhongliang Jiang, Yue Zhou, Dongliang Cao +1
The recovery of morphologically accurate anatomical images from deformed ones is challenging in ultrasound (US) image acquisition, but crucial to accurate and consistent diagnosis,…
Unsupervised Learning of Robust Spectral Shape Matching
Dongliang Cao, Paul Roetzer, Florian Bernard
We propose a novel learning-based approach for robust 3D shape matching. Our method builds upon deep functional maps and can be trained in a fully unsupervised manner. Previous dee…
Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching
Dongliang Cao, Florian Bernard
The matching of 3D shapes has been extensively studied for shapes represented as surface meshes, as well as for shapes represented as point clouds. While point clouds are a common…
Unsupervised Deep Multi-Shape Matching
Dongliang Cao, Florian Bernard
3D shape matching is a long-standing problem in computer vision and computer graphics. While deep neural networks were shown to lead to state-of-the-art results in shape matching,…