  Recent years have witnessed the rapid progress of perception algorithms on
top of LiDAR, a widely adopted sensor for autonomous driving systems. These
LiDAR-based solutions are typically data hungry, requiring a large amount of
data to be labeled for training and evaluation. However, annotating this kind
of data is very challenging due to the sparsity and irregularity of point
clouds and more complex interaction involved in this procedure. To tackle this
problem, we propose FLAVA, a systematic approach to minimizing human
interaction in the annotation process. Specifically, we divide the annotation
pipeline into four parts: find, localize, adjust and verify. In addition, we
carefully design the UI for different stages of the annotation procedure, thus
keeping the annotators to focus on the aspects that are most important to each
stage. Furthermore, our system also greatly reduces the amount of interaction
by introducing a light-weight yet effective mechanism to propagate the
annotation results. Experimental results show that our method can remarkably
accelerate the procedure and improve the annotation quality.
