ArchSym：在野外检测基于三维的建筑对称性
ArchSym: Detecting 3D-Grounded Architectural Symmetries in the Wild
提出从单张野外图像检测三维反射对称性的框架，将对称性参数化为相对于预测场景几何的有符号距离图，并利用跨视角图像匹配从SfM重建中自动构建大规模建筑对称性数据集，为三维场景理解与重建提供可复用的对称性先验和标注流程。
原文依据 a single-view symmetry detector that accurately localizes symmetries in 3D by parameterizing them as signed distance maps defined relative to predicted scene geometry
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论对称性检测是计算机视觉中的一个基本问题，对称性可作为下游任务的有力先验。然而，现有的基于学习的单图像三维对称性检测方法几乎只在以物体为中心或合成的数据集上训练和评估，因此难以泛化到真实场景。此外，由于单目输入固有的尺度模糊性，使得三维平面的定位成为一个不适定问题，许多现有工作仅预测平面的朝向。本文针对这些局限，提出了首个从单张野外RGB图像中检测基于三维的反射对称性的框架，聚焦于建筑地标。我们引入两项关键创新：（1）一个可扩展的数据标注流程，利用跨视角图像匹配从SfM重建中自动整理出大规模建筑对称性数据集ArchSym；（2）基于该数据集，构建一个单视角对称性检测器，通过将对称性参数化为相对于预测场景几何定义的有符号距离图，在三维中准确定位对称性。我们通过基于几何的替代方案验证了对称性标注流程，并证明我们的对称性检测器在新基准上显著优于最优基线。
英文原文摘要 1369 字符
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.