3D object detection is still an open problem in autonomous driving scenes. When recognizing and localizing key objects from sparse 3D inputs, autonomous vehicles suffer from a larger continuous searching space and higher fore-background imbalance compared to image-based object detection. In this paper, we aim to solve this fore-background imbalance in 3D object detection. Inspired by the recent use of focal loss in image-based object detection, we extend this hard-mining improvement of binary cross entropy to point-cloud-based object detection and conduct experiments to show its performance based on two different 3D detectors: 3D-FCN and VoxelNet. The evaluation results show up to 11.2AP gains through the focal loss in a wide range of hyperparameters for 3D object detection.
@article{arxiv.1809.06065,
title = {Focal Loss in 3D Object Detection},
author = {Peng Yun and Lei Tai and Yuan Wang and Chengju Liu and Ming Liu},
journal= {arXiv preprint arXiv:1809.06065},
year = {2019}
}
Comments
IEEE RA-L 2019 to appear. Codes and trained weights are available on the project page(https://goo.gl/2hFbmL)