FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds

TL;DR

FLAVA is a systematic annotation system for LiDAR point clouds that reduces human effort and improves quality by dividing the process into four stages with tailored interfaces and efficient result propagation.

Contribution

The paper introduces FLAVA, a novel annotation pipeline with specialized UI design and propagation mechanisms to minimize human interaction in LiDAR data labeling.

Findings

Accelerates annotation process significantly

Enhances annotation accuracy and consistency

Reduces required human effort in labeling

Abstract

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.