3D object detection has become indispensable in the field of autonomous
driving. To date, gratifying breakthroughs have been recorded in 3D object
detection research, attributed to deep learning. However, deep learning
algorithms are data-driven and require large amounts of annotated point cloud
data for training and evaluation. Unlike 2D image labels, annotating point
cloud data is difficult due to the limitations of sparsity, irregularity, and
low resolution, which requires more manual work, and the annotation efficiency
is much lower than 2D image.Therefore, we propose an annotation algorithm for
point cloud data, which is pre-annotation and camera-LiDAR late fusion
algorithm to easily and accurately annotate. The contributions of this study
are as follows. We propose (1) a pre-annotation algorithm that employs 3D
object detection and auto fitting for the easy annotation of point clouds, (2)
a camera-LiDAR late fusion algorithm using 2D and 3D results for easily error
checking, which helps annotators easily identify missing objects, and (3) a
point cloud annotation evaluation pipeline to evaluate our experiments. The
experimental results show that the proposed algorithm improves the annotating
speed by 6.5 times and the annotation quality in terms of the 3D Intersection
over Union and precision by 8.2 points and 5.6 points, respectively;
additionally, the miss rate is reduced by 31.9 points.

本文提出了一种预注释算法和相机 - LiDAR 后期融合算法来方便且准确地注释点云数据，实验结果表明该算法可以提高 6.5 倍的注释速度、提高 3D 交集超过联合和精度 8.2 点和 5.6 点，同时降低失误率 31.9 点。