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：在实际应用点云完成任务时,真正的数据质量通常比用于培训的 CAD 数据集要差得多。 少量的噪音数据通常会严重影响整个系统的准确性。 在本文中,我们提出一个质量评价网络,以便在应用完成模式之前分分点云,帮助判断点云的质量。 我们相信我们的评分方法可以帮助研究人员选择更合适的点云,以便随后完成和重建,避免人工参数调整。 此外,我们的评价模型是快速和直截了当的,可以直接插入任何模型的培训或使用过程,以便利点云的自动选择和后处理。 我们提议了一个基于 ShapeNet 的完整的数据集构建和模型评价方法。 我们用探测和流量估算任务来核查我们的网络,这是一个用于自主驱动的实时世界数据集。 实验结果表明,我们的模型可以有效地区分点云的质量,并有助于实际任务。</s>