ArchSym: Detecting 3D-Grounded Architectural Symmetries in the Wild

arXiv cs.CV / 4/27/2026

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Key Points

The paper proposes the first framework to detect 3D-grounded reflectional symmetries from single, real-world (in-the-wild) RGB images, targeting architectural landmarks.
It introduces a scalable annotation pipeline that builds a large-scale ArchSym dataset from SfM reconstructions using cross-view image matching.
The authors develop a single-view symmetry detector that localizes symmetries in 3D by representing them as signed distance maps relative to predicted scene geometry.
Experiments validate the annotation approach against geometry-based alternatives and show the detector substantially outperforms existing state-of-the-art baselines on a new benchmark.
The work is motivated by prior models’ poor generalization from object-centric/synthetic data and the ill-posed nature of monocular scale ambiguity for 3D plane localization.

Abstract

Symmetry detection is a fundamental problem in computer vision, and symmetries serve as powerful priors for downstream tasks. However, existing learning-based methods for detecting 3D symmetries from single images have been almost exclusively trained and evaluated on object-centric or synthetic datasets, and thus fail to generalize to real-world scenes. Furthermore, due to the inherent scale ambiguity of monocular inputs, which makes localizing the 3D plane an ill-posed problem, many existing works only predict the plane's orientation. In this paper, we address these limitations by presenting the first framework for detecting 3D-grounded reflectional symmetries from single, in-the-wild RGB images, focusing on architectural landmarks. We introduce two key innovations: (1) a scalable data annotation pipeline to automatically curate a large-scale dataset of architectural symmetries, ArchSym, from SfM reconstructions by leveraging cross-view image matching; and building on the dataset, (2) a single-view symmetry detector that accurately localizes symmetries in 3D by parameterizing them as signed distance maps defined relative to predicted scene geometry. We validate our symmetry annotation pipeline against geometry-based alternatives and demonstrate that our symmetry detector significantly outperforms state-of-the-art baselines on our new benchmark.