1 code implementation • 18 Apr 2024 • Viktoria Ehm, Maolin Gao, Paul Roetzer, Marvin Eisenberger, Daniel Cremers, Florian Bernard
Further, we generate a new inter-class dataset for partial-to-partial shape-matching.
no code implementations • 25 Mar 2024 • Aysim Toker, Marvin Eisenberger, Daniel Cremers, Laura Leal-Taixé
In recent years, semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery.
no code implementations • 29 Feb 2024 • Dongliang Cao, Marvin Eisenberger, Nafie El Amrani, Daniel Cremers, Florian Bernard
On the one hand, by incorporating spatial maps, our method obtains more accurate and smooth point-wise correspondences compared to previous functional map methods for shape matching.
no code implementations • 30 Sep 2023 • Christian Koke, Abhishek Saroha, Yuesong Shen, Marvin Eisenberger, Daniel Cremers
To remedy these shortcomings, we introduce ResolvNet, a flexible graph neural network based on the mathematical concept of resolvents.
no code implementations • 10 Sep 2023 • Viktoria Ehm, Paul Roetzer, Marvin Eisenberger, Maolin Gao, Florian Bernard, Daniel Cremers
Moreover, while in practice one often has only access to partial observations of a 3D shape (e. g. due to occlusion, or scanning artifacts), there do not exist any methods that directly address geometrically consistent partial shape matching.
no code implementations • ICCV 2023 • Maolin Gao, Paul Roetzer, Marvin Eisenberger, Zorah Lähner, Michael Moeller, Daniel Cremers, Florian Bernard
We propose a novel mixed-integer programming (MIP) formulation for generating precise sparse correspondences for highly non-rigid shapes.
no code implementations • CVPR 2023 • Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers
We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence.
1 code implementation • CVPR 2022 • Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Florian Bernard, Daniel Cremers
The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields.
no code implementations • 21 Apr 2022 • Abhishek Saroha, Marvin Eisenberger, Tarun Yenamandra, Daniel Cremers
Finally, we show that our adversarial training approach leads to visually plausible reconstructions that are highly consistent in recovering missing parts of a given object.
no code implementations • CVPR 2022 • Aysim Toker, Lukas Kondmann, Mark Weber, Marvin Eisenberger, Andrés Camero, Jingliang Hu, Ariadna Pregel Hoderlein, Çağlar Şenaras, Timothy Davis, Daniel Cremers, Giovanni Marchisio, Xiao Xiang Zhu, Laura Leal-Taixé
These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover (LULC) classes.
no code implementations • 29 Sep 2021 • Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Florian Bernard, Daniel Cremers
Our main contribution is deriving a simple and efficient algorithm that performs this backward pass in closed form.
no code implementations • CVPR 2021 • Marvin Eisenberger, David Novotny, Gael Kerchenbaum, Patrick Labatut, Natalia Neverova, Daniel Cremers, Andrea Vedaldi
We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i. e. in a single feed forward pass, a smooth interpolation and point-to-point correspondences between them.
1 code implementation • NeurIPS 2020 • Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers
We propose a novel unsupervised learning approach to 3D shape correspondence that builds a multiscale matching pipeline into a deep neural network.
1 code implementation • ECCV 2020 • Marvin Eisenberger, Daniel Cremers
While most prior work focuses on synthetic input shapes, our formulation is designed to be applicable to real-world scans with imperfect input correspondences and various types of noise.
1 code implementation • CVPR 2020 • Marvin Eisenberger, Zorah Lähner, Daniel Cremers
Smooth shells define a series of coarse-to-fine shape approximations designed to work well with multiscale algorithms.
1 code implementation • 27 Jun 2018 • Marvin Eisenberger, Zorah Lähner, Daniel Cremers
We present a novel method to model and calculate deformation fields between shapes embedded in $\mathbb{R}^D$.