FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds
This work is significant for autonomous driving developers and researchers who need to label large LiDAR datasets efficiently and accurately, addressing a critical bottleneck in data-hungry perception algorithms.
The paper addresses the challenge of annotating LiDAR-based point clouds for autonomous driving, which typically requires extensive manual effort due to data sparsity and irregularity. The proposed FLAVA system minimizes human interaction by dividing the annotation pipeline into four stages (find, localize, adjust, verify) and introducing an effective annotation propagation mechanism, resulting in significantly accelerated annotation and improved quality.
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.