Zorah Lähner

Zorah Lähner

Postdoc, Shape Analysis and Geometric Deep Learning

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

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Blog Post number 4

less than 1 minute read

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Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

Published:

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Blog Post number 1

less than 1 minute read

Published:

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news

portfolio

publications

Isometric Multi-Shape Matching

Published in Conference on Computer Vision and Pattern Recognition (CVPR), 2021

This paper is about the number 1. The number 2 is left for future work.

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QuAnt: Quantum Annealing with Learnt Couplings

Published in International Conference on Learning Representations (ICLR), 2023

Modern quantum annealers can find high-quality solutions to combinatorial optimisation objectives given as quadratic unconstrained binary optimisation (QUBO) problems. Unfortunately, obtaining suitable QUBO forms in computer vision remains challenging and currently requires problem-specific analytical derivations. Moreover, such explicit formulations impose tangible constraints on solution encodings. In stark contrast to prior work, this paper proposes to learn QUBO forms from data through gradient backpropagation instead of deriving them. As a result, the solution encodings can be chosen flexibly and compactly. Furthermore, our methodology is general and virtually independent of the specifics of the target problem type. We demonstrate the advantages of learnt QUBOs on the diverse problem types of graph matching, 2D point cloud alignment and 3D rotation estimation. Our results are competitive with the previous quantum state of the art while requiring much fewer logical and physical qubits, enabling our method to scale to larger problems.

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CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes

Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Accepted), 2023

Jointly matching multiple, non-rigidly deformed 3D shapes is a challenging, N P-hard problem. A perfect matching is necessarily cycle-consistent: Following the pairwise point correspondences along several shapes must end up at the starting vertex of the original shape. Unfortunately, existing quantum shape-matching methods do not support multiple shapes and even less cycle consistency. This paper addresses the open challenges and introduces the first quantum-hybrid approach for 3D shape multi-matching; in addition, it is also cycle-consistent. Its iterative formulation is admissible to modern adiabatic quantum hardware and scales linearly with the total number of input shapes. Both these characteristics are achieved by reducing the N -shape case to a sequence of three-shape matchings, the derivation of which is our main technical contribution. Thanks to quantum annealing, high-quality solutions with low energy are retrieved for the intermediate NP-hard objectives. On benchmark datasets, the proposed approach significantly outperforms extensions to multi-shape matching of a previous quantum-hybrid two-shape matching method and is on-par with classical multi-matching methods.

Conjugate Product Graphs for Globally Optimal 2D-3D Shape Matching

Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Accepted), 2023

We consider the problem of finding a continuous and non-rigid matching between a 2D contour and a 3D mesh. While such problems can be solved to global optimality by finding a shortest path in the product graph between both shapes, existing solutions heavily rely on unrealistic prior assumptions to avoid degenerate solutions (e.g. knowledge to which region of the 3D shape each point of the 2D contour is matched). To address this, we propose a novel 2D-3D shape matching formalism based on the conjugate product graph between the 2D contour and the 3D shape. Doing so allows us for the first time to consider higher-order costs, i.e. defined for edge chains, as opposed to costs defined for single edges. This offers substantially more flexibility, which we utilise to incorporate a local rigidity prior. By doing so, we effectively circumvent degenerate solutions and thereby obtain smoother and more realistic matchings, even when using only a one-dimensional feature descriptor. Overall, our method finds globally optimal and continuous 2D-3D matchings, has the same asymptotic complexity as previous solutions, produces state-of-the-art results for shape matching and is even capable of matching partial shapes.

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studentprojects

talks

teaching

Logik und Diskrete Strukturen

Mandatory lecture for Computer Science Bachelor students, University of Bonn, 2011

Teaching assistant with two weekly tutorials, supervised ~40 students.

Analysis of Three-Dimensional Shapes

Lecture for Computer Science Master students, Technical University of Munich, 2016

Organization of exercises, teaching assistant for weekly tutorial, supervised ~15 students.

Analysis of Three-Dimensional Shapes

Lecture for Computer Science Master students, Technical University of Munich, 2017

Organization of exercises, teaching assistant for weekly tutorial, supervised ~15 students.

Shape Analysis and Optimization

Seminar for Computer Science and Mathematics Master students, Technical University of Munich, 2018

Organization and supervisor in weekly seminar, supervised ~7 students.

Diskrete Strukturen

Mandatory Lecture for Computer Science (and related studies) Bachelor Students, Technical University of Munich, 2018

Teaching assistant for two weekly tutorials, supervised ~40 students.

Recent Advances in 3D Computer Vision

Seminar for Computer Science Master students, Technical University of Munich, 2020

Organization and supervisor in weekly seminar, supervised ~6 students.

Recent Advances in 3D Computer Vision

Seminar for Computer Science Master students, Technical University of Munich, 2020

Organization and supervisor in weekly seminar, supervised ~6 students.

Recent Advances in Machine Learning

Practical course for Computer Science and Mechanical Engineering Master students, University of Siegen, 2021

Supervision of project “Feature-Based Learning for 3D Correspondence”, supervised ~6 students.

Visual Computing

Seminar for Computer Science Bachelor and Master students, University of Siegen, 2021

Supervisor in weekly seminar, supervised 1 student.

Deep Learning

Lecture for Computer Science, Electrical Engineering and Mechanical Engineering Master students, University of Siegen, 2021

Organization and execution of exercises, around 100 students.

Einführung in Visual Computing

Lecture for Computer Science Bachelor students, University of Siegen, 2022

Organization and execution of exercises, around 30 students.