Tum-Façade: Reviewing and Enriching Point Cloud Benchmarks for Façade Segmentation

Abstract

Abstract. 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 façade segmentation. Robust façade 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 façade-related classes that have been designed to facilitate façade segmentation testing. We propose how to efficiently extend existing datasets and comprehensively assess their potential for façade segmentation. We use the method to create the TUM-FAÇADE dataset, which extends the capabilities of TUM-MLS-2016. Not only can TUM-FAÇADE facilitate the development of point-cloud-based façade segmentation tasks, but our procedure can also be applied to enrich further datasets.

Combining Visibility Analysis and Deep Learning for Refinement of Semantic 3d Building Models by Conflict Classification

“…The goal of semantic segmentation is to divide a point cloud into several subsets based on the semantics of the points. Following Wysocki et al (2022b) and as shown in Figure 3, eight relevant classes for fac ¸ade segmentation and reconstruction tasks are considered: arch (dark blue), column (red), molding (purple), floor (green), door (brown), window (blue), wall (beige), and other (gray).…”

“…The open TUM-MLS-2016 dataset (Zhu et al, 2020) was transformed into the global coordinate reference system (CRS) and used to perform point cloud ray tracing. The TUM-FAC ¸ADE dataset was deployed for training, as it comprises fac ¸ade-annotated point clouds (Wysocki et al, 2022b). For computational reasons, we subsampled the original dataset removing all the redundant points within a 5 cm distance.…”

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

“…Even though these datasets, which often consist of both 3D and 2D data, have a specific category of 'building', they do not typically include sub-categories, such as windows or doors. This is, however, slowly changing and since 2020 there has been a growing interest in point cloud data that includes façade-level classes, such as windows, doors, balconies [158,159]. Benchmark datasets based only on images contain more detailed classes, which sometimes include windows and doors, however, they often lack the internal and external parameters, therefore making 3D reconstruction of building façades challenging.…”