Gernot Riegler
Research Scientist at Third Dimension AI
Graz, Styria, Austria
Summary
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Rockstar🎓
Top SchoolGernot Riegler is a senior research scientist based in Graz, Austria, with 11 years of experience at the intersection of computer vision and machine learning, currently working on 3D reconstruction and material estimation. He has held research roles at industry leaders including Unity, Amazon, Intel Labs, Luma AI, and now Third Dimension AI, combining academic rigor from a PhD at TU Graz with product-focused R&D. His contributions to Open3D—implementing and optimizing core mesh processing algorithms such as Loop subdivision, quadric minimization, and Poisson disk sampling—highlight a rare blend of low-level geometry engineering and neural rendering expertise. Gernot’s work spans both foundational algorithm design and practical back-end implementations, enabling scalable 3D data processing for real-world systems. He often bridges theory and production, turning complex 3D problems into efficient, deployable solutions.
10 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Technische Universität Graz
German, English
Github Skills (12)
algorithm10
data-structures10
algorithms10
c-language10
cprogramming-language10
data-structure10
mesh-processing10
3d9
computer-graphics9
point-cloud9
cpp9
delaunay-triangulation9
Programming languages (5)
Github contributions (5)
isl-org/Open3D
Apr 2019 - Jul 2020
Open3D: A Modern Library for 3D Data Processing
Role in this project:
Back-end Developer & Algorithm Engineer
Back-end Developer & Algorithm EngineerContributions:37 reviews, 71 commits, 107 PRs in 1 year 2 months
Contributions summary:Gernot primarily contributed to the development and optimization of core algorithms within the Open3D library. Their work includes implementing and refining mesh processing algorithms, such as uniform point sampling, mesh simplification using vertex clustering, quadric minimization, and Loop subdivision, as well as the integration of Poisson disk sampling. They also implemented methods to check mesh properties and added new classes for bounding volumes.
python3d-perceptionodometrytensorflowpointcloud
griegler/griegler.github.io
Sep 2015 - Jun 2021
Contributions:43 pushes, 1 branch in 5 years 9 months
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