Daniel Cremers

TL;DR: In this paper, a method for computing partial functional correspondence between non-rigid shapes is proposed, which uses perturbation analysis to show how removal of shape parts changes the Laplace-Beltrami eigenfunctions, and exploit it as a prior on the spectral representation of the correspondence.

TL;DR: This paper proposes a simple, yet effective method to compensate for large delays in the control loop using an accurate model of the quadrocopter’s flight dynamics, and presents a novel, closed-form method to estimate the scale of a monocular SLAM system from additional metric sensors.

TL;DR: The main contribution is to decouple the position and velocity estimation steps, and to estimate dense velocities using a variational approach, which provides dense velocity estimates with accurate results at distances up to 50 meters.

TL;DR: This paper revisits regularization and shows that appropriate adaptive regularization substantially improves the accuracy of estimated motion fields and systematically evaluates regularizes which adoptively favor rigid body motion (if supported by the image data) and motion field discontinuities that coincide with discontinUities of the image structure.

TL;DR: The TUM VI dataset as mentioned in this paper is a dataset with a diverse set of sequences in different scenes for evaluating visual-inertial (VI) odometry and SLAM methods in domains such as augmented reality or robotics.