An Efficient Semi-Automated Scheme for LiDAR Annotation and A Benchmark Infrastructure Dataset

We present an efficient semi-automated annotation tool that automatically annotates LiDAR sequences with tracking algorithms while offering a fully annotated infrastructure LiDAR dataset---FLORIDA (Florida LiDAR-based Object Recognition and Intelligent Data Annotation)---which will be made publicly available. Our advanced annotation tool seamlessly integrates multi-object tracking (MOT), single-object tracking (SOT), and batch editing functionalities. Specifically, we introduce a human-in-the-loop schema where annotations are incrementally added to the training set of MOT and SOT models after being fixed and improved by human annotators. By repeating the process, we significantly increase the overall annotation speed by $3- 4$ times and obtain higher quality annotations than a state-of-the-art annotation tool. The human annotation experiments verify the effectiveness of our annotation tool. In addition, we provide detailed statistics and object detection evaluation results for our benchmark dataset at a busy traffic intersection.