A Conditional Confidence Calibration Method for 3D Point Cloud Object Detection

When we apply neural networks to safety-critical systems such as self-driving cars, the reliability of their predictions must be considered. However, recent deep neural networks have tended to output biased confidence. Additionally, the extent of confidence bias estimated by object detectors varies depending on factors such as the detected object’s position and size. To address this problem, many researchers have proposed methods for calibrating confidences estimated by object detectors. In this study, we investigate the factors that may cause bias in the confidence of LiDAR-based 3D object detectors and show that our calibration method compensates for the effect of these factors to provide reliable confidence estimations, regardless of the neural network model used or the situations in which objects are detected.