Focal Loss in 3D Object Detection

3D object detection is still an open problem in autonomous driving scenes. Robots recognize and localize key objects from sparse inputs, and suffer from a larger continuous searching space as well as serious fore-background imbalance compared to the image-based detection. In this paper, we try to solve the fore-background imbalance in the 3D object detection task. Inspired by the recent improvement of focal loss on image-based detection which is seen as a hard-mining improvement of binary cross entropy, we extend it to point-cloud-based object detection and conduct experiments to show its performance based on two different type of 3D detectors: 3D-FCN and VoxelNet. The results show up to 11.2 AP gains from focal loss in a wide range of hyperparameters in 3D object detection. Our code is available at https://github.com/pyun-ram/FL3D.