Scott Ettinger

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: In this article, the authors introduce a large-scale interactive motion dataset with over 100,000 scenes, each 20 seconds long at 10 Hz, collected by mining for interesting interactions between vehicles, pedestrians, and cyclists across six cities within the United States.

TL;DR: In this article, a large scale, high quality, and diverse dataset for self-driving data is presented, which consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies.