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.

中文翻译：

---

FLAVA：查找，定位，调整和验证以注释基于LiDAR的点云

近年来，见证了基于LiDAR的感知算法的飞速发展，LiDAR是一种广泛应用于自动驾驶系统的传感器。这些基于LiDAR的解决方案通常需要大量数据，需要对大量数据进行标记以进行培训和评估。但是，由于点云的稀疏性和不规则性以及此过程涉及的更复杂的交互作用，注释此类数据非常具有挑战性。为了解决这个问题，我们提出了FLAVA，这是一种在注释过程中最大程度地减少人与人之间互动的系统方法。具体来说，我们将注释管道分为四个部分：查找，定位，调整和验证。此外，我们为注释过程的不同阶段精心设计了UI，从而使注释者专注于每个阶段最重要的方面。此外，我们的系统还通过引入一种轻量而有效的机制来传播注释结果，从而大大减少了交互量。实验结果表明，该方法可以显着加快处理速度，提高标注质量。