In this work, we introduce a practical method for reducing big point clouds of buildings and infrastructure. The proposed method introduces bilateral filtering with a tailored set of evaluation functions to conserve maximum information. The statistical parameters necessary for our model are selected by examining various point properties of a comprehensive dataset. The dataset contains artificial, photogrammetric and laser-scanned point clouds and has been made publicly available. For verification, we showcase our filtering method by preserving more information than voxel grid or density filters, enabling even sparser photogrammetric datasets. Finally, we discuss some encoding strategies as well as the best balance between size and resolution.

在这项工作中，我们介绍了一种减少建筑物和基础设施大点云的实用方法。所提出的方法引入了带有定制评估函数集的双边过滤，以节省最大的信息。通过检查综合数据集的各个点属性来选择模型所需的统计参数。该数据集包含人工，摄影测量和激光扫描的点云，并已公开提供。为了进行验证，我们通过保留比体素网格或密度过滤器更多的信息来展示我们的过滤方法，甚至启用了稀疏的摄影测量数据集。最后，我们讨论一些编码策略以及大小和分辨率之间的最佳平衡。