Learning Monocular 3D Vehicle Detection Without 3D Bounding Box Labels. - Lukas Koestler; Nan Yang 007; Rui Wang 037; Daniel Cremers (GCPR, 2020)

TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo. - Lukas Koestler; Nan Yang 007; Niclas Zeller; Daniel Cremers (CoRL, 2021)

The Probabilistic Normal Epipolar Constraint for Frame- To-Frame Rotation Optimization under Uncertain Feature Positions. - Dominik Muhle; Lukas Koestler; Nikolaus Demmel; Florian Bernard; Daniel Cremers (CVPR, 2022)

Intrinsic Neural Fields: Learning Functions on Manifolds. - Lukas Koestler; Daniel Grittner; Michael Möller 001; Daniel Cremers; Zorah Lähner (ECCV (2), 2022)

Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares. - Dominik Muhle; Lukas Koestler; Krishna Murthy Jatavallabhula; Daniel Cremers (CVPR, 2023)

Neural Implicit Representations for Physical Parameter Inference from a Single Video. - Florian Hofherr; Lukas Koestler; Florian Bernard; Daniel Cremers (WACV, 2023)

E-NeRF: Neural Radiance Fields From a Moving Event Camera. - Simon Klenk; Lukas Koestler; Davide Scaramuzza 001; Daniel Cremers (IEEE Robotics Autom. Lett., 2023)

Deep Event Visual Odometry. - Simon Klenk; Marvin Motzet; Lukas Koestler; Daniel Cremers (3DV, 2024)

Masked Event Modeling: Self-Supervised Pretraining for Event Cameras. - Simon Klenk; David Bonello; Lukas Koestler; Nikita Araslanov; Daniel Cremers (WACV, 2024)