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.

翻译：在点云补全任务的实际应用中，真实数据质量通常远逊于用于训练的CAD数据集。少量噪声数据往往会对整体系统精度产生显著影响。本文提出一种质量评估网络，在应用补全模型前对点云进行评分，以辅助判断点云质量。我们相信该评分方法能帮助研究者筛选更适宜的点云进行后续补全与重建，并避免手动参数调整。此外，我们的评估模型快速简洁，可直接嵌入任意模型的训练或使用流程，便于点云的自动筛选与后处理。我们提出一套基于ShapeNet的完整数据集构建与模型评估方法，并在自动驾驶真实场景数据集KITTI上，通过目标检测与光流估计任务验证了该网络的有效性。实验结果表明，该模型能有效区分点云质量，并在实际任务中发挥重要作用。