  Current perception systems often carry multimodal imagers and sensors such as
2D cameras and 3D LiDAR sensors. To fuse and utilize the data for downstream
perception tasks, robust and accurate calibration of the multimodal sensor data
is essential. We propose a novel deep learning-driven technique (CalibDNN) for
accurate calibration among multimodal sensor, specifically LiDAR-Camera pairs.
The key innovation of the proposed work is that it does not require any
specific calibration targets or hardware assistants, and the entire processing
is fully automatic with a single model and single iteration. Results comparison
among different methods and extensive experiments on different datasets
demonstrates the state-of-the-art performance.
