Henrik Kretzschmar

TL;DR: This work introduces a new large scale, high quality, diverse dataset, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies, and studies the effects of dataset size and generalization across geographies on 3D detection methods.

TL;DR: In this paper, a large scale, high quality, and diverse dataset for self-driving data is presented, consisting of LiDAR and camera data captured across a range of urban and suburban geographies.

TL;DR: An extensive set of experiments suggests that the technique outperforms state-of-the-art methods to model the behavior of pedestrians, which also makes it applicable to fields such as behavioral science or computer graphics.

TL;DR: This paper presents a novel approach to predict the movements of pedestrians that applies a maximum entropy learning method based on features that capture relevant aspects of the trajectories to determine the probability distribution that underlies human navigation behavior.

TL;DR: It is demonstrated that when scaling NeRF to render city-scale scenes spanning multiple blocks, it is vital to de-compose the scene into individually trained NeRFs, which decouples rendering time from scene size, enables rendering to scale to arbitrarily large environments, and allows per-block updates of the environment.