In the practical application of point cloud completion tasks, real data
quality is usually much worse than the CAD datasets used for training. A small
amount of noisy data will usually significantly impact the overall system's
accuracy. In this paper, we propose a quality evaluation network to score the
point clouds and help judge the quality of the point cloud before applying the
completion model. We believe our scoring method can help researchers select
more appropriate point clouds for subsequent completion and reconstruction and
avoid manual parameter adjustment. Moreover, our evaluation model is fast and
straightforward and can be directly inserted into any model's training or use
process to facilitate the automatic selection and post-processing of point
clouds. We propose a complete dataset construction and model evaluation method
based on ShapeNet. We verify our network using detection and flow estimation
tasks on KITTI, a real-world dataset for autonomous driving. The experimental
results show that our model can effectively distinguish the quality of point
clouds and help in practical tasks.

本文提出了一种质量评估网络，用于评分点云以帮助研究人员选择更适合的点云，并验证了该网络在自动驾驶领域的实用性。