ZAHA: Introducing the Level of Facade Generalization and the Large-Scale Point Cloud Facade Semantic Segmentation Benchmark Dataset

TL;DR

ZAHA introduces a hierarchical facade class system and the largest 3D facade segmentation dataset, advancing urban modeling and digital twin development by providing comprehensive, standardized data for facade analysis.

Contribution

It presents the Level of Facade Generalization (LoFG) hierarchy and a large-scale annotated dataset, addressing variability and standardization in facade segmentation.

Findings

Baseline methods show varying performance on LoFG classes.

The dataset enables benchmarking and comparison of facade segmentation techniques.

Discussion highlights unresolved challenges in facade segmentation.

Abstract

Facade semantic segmentation is a long-standing challenge in photogrammetry and computer vision. Although the last decades have witnessed the influx of facade segmentation methods, there is a lack of comprehensive facade classes and data covering the architectural variability. In ZAHA, we introduce Level of Facade Generalization (LoFG), novel hierarchical facade classes designed based on international urban modeling standards, ensuring compatibility with real-world challenging classes and uniform methods' comparison. Realizing the LoFG, we present to date the largest semantic 3D facade segmentation dataset, providing 601 million annotated points at five and 15 classes of LoFG2 and LoFG3, respectively. Moreover, we analyze the performance of baseline semantic segmentation methods on our introduced LoFG classes and data, complementing it with a discussion on the unresolved challenges for facade segmentation. We firmly believe that ZAHA shall facilitate further development of 3D facade semantic segmentation methods, enabling robust segmentation indispensable in creating urban digital twins.