Deep Fusion of Camera and LIDAR

Overview & Synopsis

Fusing camera and LIDAR data in autonomous driving poses challenges such as accurate calibration, differing data representations, and extensive training data requirements. This dissertation addresses these by three contributions: a deep neural network for LIDAR-to-camera calibration, two depth completion approaches for processing sparse depth measurements in the image space, and a large-scale dataset of 93k RGB and depth images for training and evaluating deep networks.