Benjamin Caine

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: This work presents an object detection system called StarNet designed specifically to take advantage of the sparse and 3D nature of point cloud data, and shows how this design leads to competitive or superior performance on the large Waymo Open Dataset and the KITTI detection dataset, as compared to convolutional baselines.

TL;DR: This paper proposes two novel techniques: InverseAug that inverses geometric-related augmentations, e.g., rotation, to enable accurate geometric alignment between lidar points and image pixels, and LearnableAlign that leverages cross-attention to dynamically capture the correlations between image and lidar features during fusion.

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.