# ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point Clouds

Source: [ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point Clouds](<https://www.sota2.com/research/paper/68fa73751e209131cea5ca79>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:50:15.439Z

Authors: Ka Lung Cheung, Chi Chung Lee

Published: 2024-06-03T14:02:23.000Z

## Abstract

Precise segmentation of architectural structures provides detailed information about various building components, enhancing our understanding and interaction with our built environment. Nevertheless, existing outdoor 3D point cloud datasets have limited and detailed annotations on architectural exteriors due to privacy concerns and the expensive costs of data acquisition and annotation. To overcome this shortfall, this paper introduces a semantically-enriched, photo-realistic 3D architectural models dataset and benchmark for semantic segmentation. It features 4 different building purposes of real-world buildings as well as an open architectural landscape in Hong Kong. Each point cloud is annotated into one of 14 semantic classes.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.01337>)

[PDF](<https://arxiv.org/pdf/2406.01337.pdf>)