Focal Loss in 3D Object Detection
This addresses a specific challenge in autonomous driving by improving detection accuracy, but it is incremental as it adapts an existing technique to a new domain.
The paper tackled the foreground-background imbalance problem in 3D object detection for autonomous driving by extending focal loss from image-based detection to point-cloud-based methods, resulting in up to 11.2 AP gains.
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.