  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.
