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:点云数据被广泛认为是城市制图目的下最佳的数据集类型之一。因此,点云数据集通常被用作各种城市解释方法的基准类型。然而,鲜有研究者探讨点云基准用于外墙分割的用途。健壮的外墙分割正在成为各种应用的关键因素,从模拟自动驾驶功能到保护文化遗产。在这项工作中,我们提出了一种方法,通过增加与外墙相关的类别来丰富现有的点云数据集,这些类别旨在促进外墙分割测试。我们提出了如何高效地扩展现有数据集并全面评估其外墙分割性能的潜力。我们使用这种方法创建了TUM-FAÇADE数据集,该数据集扩展了TUM-MLS-2016的能力。TUM-FAÇADE不仅可以促进基于点云的外墙分割任务的开发,而且我们的过程也可以应用于丰富其他数据集。