论文标题
Flava：查找，本地化，调整和验证以注释基于激光雷达的点云
FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds
论文作者
论文摘要
近年来，见证了人们在Lidar上的感知算法的快速进步，这是一种广泛采用的自主驾驶系统的传感器。这些基于激光雷达的解决方案通常是饥饿的数据，需要大量数据标记用于培训和评估。但是，由于点云的稀疏性和不规则性以及此过程中涉及的更复杂的相互作用，对这种数据进行注释非常具有挑战性。为了解决这个问题，我们提出了Flava，这是一种系统的方法，可以最大程度地减少注释过程中的人类相互作用。具体而言，我们将注释管道分为四个部分：查找，本地化，调整和验证。此外，我们将UI仔细设计为注释过程的不同阶段，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轻巧但有效的机制来传播注释结果，从而大大减少了相互作用的量。实验结果表明，我们的方法可以显着加速该过程并提高注释质量。
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.
