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

• URL: http://arxiv.org/abs/2604.22202v1
• Date: Fri, 24 Apr 2026 04:12:03 GMT
• Title: ArchSym: Detecting 3D-Grounded Architectural Symmetries in the Wild
• Abstract summary: We present the first framework for detecting 3D-grounded reflectional symmetries from single, in-the-wild RGB images, focusing on architectural landmarks.<n>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 (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.
• Score: 29.918676050874964
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SeeSE3: Emergence of 3D Space in Vision Features [54.140615953727355]
  We investigate the relation between the structure of the space of visual features and the group of Euclidean transformations $SE(3)$.<n>We show that self-supervised vision models, which, in principle, have not been trained with direct 3D supervision or active agency, possess latent subspaces that are remarkably strongly correlated with 3D Euclidean space, when probed correctly.<n>We propose a new class of "Latent-Space Navigation" techniques that perform visual odometry and localization purely in the latent space, bypassing the need for explicit 3D reconstruction.
  arXiv  Detail & Related papers  (2026-07-15T18:00:31Z)
• Learning 3D Representations for Spatial Intelligence from Unposed Multi-View Images [81.94999489820974]
  UniSplat is a feed-forward framework for learning 3D representations from unposed multi-view images.<n>We introduce a dual-masking strategy that strengthens geometry induction in the encoder.<n>Second, we develop a coarse-to-fine Gaussian splatting strategy that reduces appearance-semantics inconsistencies.<n>Third, we introduce a pose-conditioned recalibration mechanism that interrelates predicted 3D point and semantic maps into the image plane.
  arXiv  Detail & Related papers  (2026-04-12T10:36:18Z)
• PLANA3R: Zero-shot Metric Planar 3D Reconstruction via Feed-Forward Planar Splatting [56.188624157291024]
  We introduce PLANA3R, a pose-free framework for metric Planar 3D Reconstruction from unposed two-view images.<n>Unlike prior feedforward methods that require 3D plane annotations during training, PLANA3R learns planar 3D structures without explicit plane supervision.<n>We validate PLANA3R on multiple indoor-scene datasets with metric supervision and demonstrate strong generalization to out-of-domain indoor environments.
  arXiv  Detail & Related papers  (2025-10-21T15:15:33Z)
• Zero-shot Inexact CAD Model Alignment from a Single Image [53.37898107159792]
  A practical approach to infer 3D scene structure from a single image is to retrieve a closely matching 3D model from a database and align it with the object in the image.<n>Existing methods rely on supervised training with images and pose annotations, which limits them to a narrow set of object categories.<n>We propose a weakly supervised 9-DoF alignment method for inexact 3D models that requires no pose annotations and generalizes to unseen categories.
  arXiv  Detail & Related papers  (2025-07-04T04:46:59Z)
• Training-free zero-shot 3D symmetry detection with visual features back-projected to geometry [0.6445605125467574]
  We present a training-free approach for zero-shot 3D symmetry detection that leverages visual features from foundation vision models such as DINOv2.<n>Our work demonstrates how foundation vision models can help in solving complex 3D geometric problems such as symmetry detection.
  arXiv  Detail & Related papers  (2025-05-30T03:09:18Z)
• Symmetry Strikes Back: From Single-Image Symmetry Detection to 3D Generation [29.732780338284353]
  We introduce Reflect3D, a scalable, zero-shot symmetry detector capable of robust generalization to diverse and real-world scenarios.<n>We show the practical benefit of incorporating detected symmetry into single-image 3D generation pipelines.
  arXiv  Detail & Related papers  (2024-11-26T04:14:31Z)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices.<n>Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• Partial Symmetry Detection for 3D Geometry using Contrastive Learning with Geodesic Point Cloud Patches [10.48309709793733]
  We propose to learn rotation, reflection, translation and scale invariant local shape features for geodesic point cloud patches. We show that our approach is able to extract multiple valid solutions for this ambiguous problem. We incorporate the detected symmetries together with a region growing algorithm to demonstrate a downstream task.
  arXiv  Detail & Related papers  (2023-12-13T15:48:50Z)
• NeRD: Neural 3D Reflection Symmetry Detector [27.626579746101292]
  We present NeRD, a Neural 3D Reflection Symmetry Detector. We first enumerate the symmetry planes with a coarse-to-fine strategy and then find the best ones by building 3D cost volumes. Our experiments show that the symmetry planes detected with our method are significantly more accurate than the planes from direct CNN regression.
  arXiv  Detail & Related papers  (2021-04-19T17:25:51Z)
• M3DSSD: Monocular 3D Single Stage Object Detector [82.25793227026443]
  We propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. The proposed M3DSSD achieves significantly better performance than the monocular 3D object detection methods on the KITTI dataset.
  arXiv  Detail & Related papers  (2021-03-24T13:09:11Z)
• SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images [26.38270361331076]
  We propose an end-to-end deep neural network which is able to predict both reflectional and rotational symmetries of 3D objects. We also contribute a benchmark of 3D symmetry detection based on single-view RGB-D images.
  arXiv  Detail & Related papers  (2020-08-02T14:10:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.