Point clouds are widely regarded as one of the best dataset types for urban mapping purposes. Hence, point cloud datasets are commonly investigated as benchmark types for various urban interpretation methods. Yet, few researchers have addressed the use of point cloud benchmarks for fa\c{c}ade segmentation. Robust fa\c{c}ade segmentation is becoming a key factor in various applications ranging from simulating autonomous driving functions to preserving cultural heritage. In this work, we present a method of enriching existing point cloud datasets with fa\c{c}ade-related classes that have been designed to facilitate fa\c{c}ade segmentation testing. We propose how to efficiently extend existing datasets and comprehensively assess their potential for fa\c{c}ade segmentation. We use the method to create the TUM-FA\c{C}ADE dataset, which extends the capabilities of TUM-MLS-2016. Not only can TUM-FA\c{C}ADE facilitate the development of point-cloud-based fa\c{c}ade segmentation tasks, but our procedure can also be applied to enrich further datasets.

翻译：点云被广泛认为是城市测绘中最理想的数据集类型之一。因此，点云数据集常被用作各类城市场景解译方法的基准。然而，针对立面分割这一特定任务，利用点云基准的研究仍较为有限。稳健的立面分割正逐渐成为从自动驾驶功能模拟到文化遗产保护等众多应用中的关键因素。本文提出一种方法，通过为现有点云数据集添加专为立面分割测试设计的立面相关类别，实现数据集的丰富。我们提出如何高效扩展现有数据集，并全面评估其在立面分割任务中的潜力。利用该方法，我们构建了TUM-FAÇADE数据集，扩展了TUM-MLS-2016数据集的能力。TUM-FAÇADE不仅能促进基于点云的立面分割任务的发展，我们的流程还可用于进一步丰富其他数据集。