  Cameras and LiDARs are both important sensors for autonomous driving, playing
critical roles in 3D object detection. Camera-LiDAR Fusion has been a prevalent
solution for robust and accurate driving perception. In contrast to the vast
majority of existing arts that focus on how to improve the performance of 3D
target detection through cross-modal schemes, deep learning algorithms, and
training tricks, we devote attention to the impact of sensor configurations on
the performance of learning-based methods. To achieve this, we propose a
unified information-theoretic surrogate metric for camera and LiDAR evaluation
based on the proposed sensor perception model. We also design an accelerated
high-quality framework for data acquisition, model training, and performance
evaluation that functions with the CARLA simulator. To show the correlation
between detection performance and our surrogate metrics, We conduct experiments
using several camera-LiDAR placements and parameters inspired by self-driving
companies and research institutions. Extensive experimental results of
representative algorithms on nuScenes dataset validate the effectiveness of our
surrogate metric, demonstrating that sensor configurations significantly impact
point-cloud-image fusion based detection models, which contribute up to 30%
discrepancy in terms of the average precision.
