James Pritts

About

James Pritts is a Marie Skłodowska-Curie Actions Fellow in the Marine Data Science group at Kiel University’s Institute of Computer Science. His research focuses on robust geometric estimation for computer vision, including RANSAC-based model fitting, camera calibration, and multi-view geometry, with applications ranging from conventional imagery to challenging underwater environments.

Before joining Kiel University, he was an Assistant Professor at Ukrainian Catholic University, where he conducted research in 3D computer vision and helped establish the Ph.D. program in Applied Sciences. He previously worked at the Czech Institute of Informatics, Robotics and Cybernetics and at Facebook Reality Labs, where he developed geometric calibration methods for head-mounted capture systems.

His work has received the ACCV 2018 Saburo Tsuji Best Paper Award and the IVCNZ 2013 Best Paper Award. He is also a member of the European Laboratory for Learning and Intelligent Systems. Earlier in his career, he served as Lead Engineer at BAE Systems on DARPA-funded computer-vision projects and as a research fellow at NASA, where he developed prototype gesture-recognition software for the International Space Station.

News

• Jun 2026 New preprint: RANSAC Scoring Done Right is on arXiv.
• Jun 2026 Till Sittart completed his M.Sc. thesis, Affine-Equivariant Blob Targets for Robust Absolute Pose in Robotics.
• Dec 2025 Awarded €10,000 from Kiel Marine Science (KMS) for Enhancing Underwater Robotics for Visual Mapping in Turbid Environments.
• Nov 2025 Liam Boddin completed his M.Sc. thesis, Underwater Calibration of Near-point Anisotropic Lights.
• Sep 2024 Joined CAU Kiel as a Marie Skłodowska-Curie (MSCA) Fellow.

Publications

RANSAC Scoring Done Right

J. Pritts, F. Seegräber, K. Köser

arXiv preprint arXiv:2606.27385, 2026

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abstract

The most widely used RANSAC variants score candidate models by counting inliers or summing per-point scores that saturate beyond a residual threshold. Every such score requires a user-supplied parameter that is a function of the inlier scale, which must itself be estimated from contaminated data. We remove this dependence by reversing the usual order of inference: rather than estimating the scale and then scoring against it, we marginalize the inlier scale analytically in closed form under a conjugate Inverse-Gamma prior for a fixed inlier partition, then optimize over partitions. A single closed-form expression spans the non-informative Jeffreys limit and informative empirical-Bayes priors, so the same score adapts across data-rich and data-scarce regimes without any change to the algorithm. The proposed RANSAC score is the first in which the inlier scale is genuinely absent from the formula. The score admits O(N log N) computation via sort-and-sweep. On a benchmark of nearly 70,000 image pairs spanning different two-view estimation problems and both engineered and learned feature pipelines, the proposed score exceeds the state of the art (RANSAC, MSAC, GaU, MAGSAC): it stays nearly flat under threshold miscalibration where baselines degrade, reaches near-optimal accuracy from as few as two validation pairs where baselines need on the order of 100 times more, and tightens its prior regularization as validation data grows scarce.

bibtex

@article{pritts2026ransac,
  title   = {RANSAC Scoring Done Right},
  author  = {Pritts, James and Seegr{\"a}ber, Felix and K{\"o}ser, Kevin},
  journal = {arXiv preprint arXiv:2606.27385},
  year    = {2026}
}

Information-Theoretic Online Multi-Camera Extrinsic Calibration

E. Dexheimer, P. Peluse, J. Chen, J. Pritts, and M. Kaess

IEEE Robotics and Automation Letters, 2022

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BabelCalib: A Universal Approach to Calibrating Central Cameras

Y. Lochman, K. Liepieshov, J. Chen, M. Perdoch, C. Zach, J. Pritts

International Conference on Computer Vision (ICCV) 2021

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Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration

Y. Lochman, O. Dobosevych, R. Hyrniv, J. Pritts

Winter Conference on Applications of Computer Vision (WACV) 2021

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Minimal Solvers for Rectifying from Radially-Distorted Conjugate Translations

J. Pritts, Z. Kukelova, V. Larsson, Y. Lochman, O. Chum

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2021

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Minimal Solvers for Rectifying from Radially-Distorted Scales and Change of Scales

J. Pritts, Z. Kukelova, V. Larsson, Y. Lochman, O. Chum

International Journal of Computer Vision (IJCV) 2020

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Rectification from Radially-Distorted Scales

J. Pritts, Z. Kukelova, V. Larsson, O. Chum

Asian Conference on Computer Vision (ACCV) 2018

★ Best Paper Award

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Radially-Distorted Conjugate Translations

J. Pritts, Z. Kukelova, V. Larsson, O. Chum

Conference on Computer Vision and Pattern Recognition (CVPR) 2018

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Coplanar Repeats by Energy Minimization

J. Pritts, D. Rozumnyi, M. P. Kumar, O. Chum

British Machine Vision Conference (BMVC) 2016

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Detection, Rectification and Segmentation of Coplanar Repeated Patterns

J. Pritts, O. Chum, J. Matas

Conference on Computer Vision and Pattern Recognition (CVPR) 2014

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Approximate Models for Fast and Accurate Epipolar Geometry Estimation

J. Pritts, O. Chum, J. Matas

Image and Vision Computing New Zealand (IVCNZ) 2013

★ Best Paper Award

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Awards

ACCV 2018 Saburo Tsuji Best Paper

Rectification from Radially-Distorted Scales

IVCNZ 2013 Best Paper

Approximate Models for Fast and Accurate Epipolar Geometry Estimation

Advising

M.Sc. Students

B.Sc. Students

Teaching