3 papers
cs.CV2022
The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions
Dominik Muhle, Lukas Koestler, Nikolaus Demmel +2
The estimation of the relative pose of two camera views is a fundamental problem in computer vision. Kneip et al. proposed to solve this problem by introducing the normal epipolar…
cs.CV2022
Intrinsic Neural Fields: Learning Functions on Manifolds
Lukas Koestler, Daniel Grittner, Michael Moeller +2
Neural fields have gained significant attention in the computer vision community due to their excellent performance in novel view synthesis, geometry reconstruction, and generative…
cs.CV2020
Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels
L. Koestler, N. Yang, R. Wang +1
The training of deep-learning-based 3D object detectors requires large datasets with 3D bounding box labels for supervision that have to be generated by hand-labeling. We propose a…