UnderOneFacade: Worldwide Facade Semantic Segmentation Benchmark Dataset

MCML Authors

Benjamin Busam

Prof. Dr.

Core PI

Photogrammetry and Remote Sensing

Abstract

Globally consistent semantic digital twins require centimeter-accurate and geographically transferable 3D facade segmentation. However, progress in facade parsing is limited by the lack of large-scale, standardized benchmarks for evaluating cross-domain generalization. Existing datasets are geographically narrow, semantically inconsistent, or insufficiently precise. We introduce UnderOneFacade, the largest cross-country and cross-continent 3D facade benchmark to date, comprising centimeter-accurate point clouds with hierarchical, harmonized, and architecturally grounded semantic labels totaling 2.7 billion annotated points. Through a systematic evaluation of representative point-, graph- and transformer-based architectures, we show that current methods struggle to recognize fine-grained architectural elements and degrade significantly across geographic domains, with the best models achieving only up to 33 IoU on the fine-grained LoFG3 benchmark. By combining geometric precision with standardized semantics at unprecedented scale, UnderOneFacade establishes a rigorous benchmark for developing robust and transferable 3D segmentation models. The dataset, evaluation scripts, and pretrained models will be released upon publication.

inproceedings WWG+26a

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ECCV 2026

19th European Conference on Computer Vision. Malmö, Sweden, Sep 08-12, 2026. To be published. Preprint available.

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Authors

Y. Wang • F. Wang • P. Gyawali • Z. Xu • A. Klimkowska • Y. Jing • W. Yang • F. Biljecki • C. Holst • B. Busam • B. Sheil • O. Wysocki

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Links

arXiv GitHub

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Research Area

 B1 | Computer Vision