Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
This addresses the tedious and time-consuming annotation of 3D LiDAR data for autonomous driving, but it is incremental as it builds on existing active learning and object detection techniques.
The paper tackles the problem of reducing annotation efforts for training a LiDAR 3D object detector in autonomous driving by proposing an active learning method that uses 2D region proposals from RGB images to speed up learning, achieving up to 60% savings in labeling while maintaining the same performance.
Training a deep object detector for autonomous driving requires a huge amount of labeled data. While recording data via on-board sensors such as camera or LiDAR is relatively easy, annotating data is very tedious and time-consuming, especially when dealing with 3D LiDAR points or radar data. Active learning has the potential to minimize human annotation efforts while maximizing the object detector's performance. In this work, we propose an active learning method to train a LiDAR 3D object detector with the least amount of labeled training data necessary. The detector leverages 2D region proposals generated from the RGB images to reduce the search space of objects and speed up the learning process. Experiments show that our proposed method works under different uncertainty estimations and query functions, and can save up to 60% of the labeling efforts while reaching the same network performance.