Noah Snavely#1104
Excited to share ArchSym at #CVPR2026! 🏛️
Existing 3D symmetry detectors work well on clean, object-centric data. But what about in the wild?
In our work, we tackle 3D-grounded reflectional symmetry detection specifically for real-world architectural landmarks.
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We use a sort of @elliottszwu-style strategy to build a dataset of symmetries in architectural scenes, then detect and localize symmetries in new images via signed-distance functions. This is really great work from Hanyu with very nice visuals!
Excited to share ArchSym at #CVPR2026! 🏛️
Existing 3D symmetry detectors work well on clean, object-centric data. But what about in the wild?
In our work, we tackle 3D-grounded reflectional symmetry detection specifically for real-world architectural landmarks.
🧵[1/7]
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On held-out landmark scenes, our method improves over both Reflect3D (finetuned on our dataset), and a simple baseline that directly regresses plane parameters from the same VGGT features.
See our paper for full quantitative results and evaluation details.
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While a failure mode for SfM, these “illusory” matches are the exact correspondences needed to define a building's symmetry.
To find reflectional symmetries, we simply *flip* one image before matching. We lift matches to 3D, fit planes, and cluster them into annotations.
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Moving into the wild raises a practical question: how do we find & annotate real symmetric structures?
Architectural landmarks are a perfect starting point, and the “doppelganger” problem gives us a clue: image matchers easily confuse visually similar building structures.
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We then build a symmetry detector based on VGGT, a powerful 3D foundation model.
From a single image, scene scale is ambiguous. So instead of predicting a plane in some arbitrary coordinate system, we ground the symmetry plane relative to VGGT’s predicted point map.
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Specifically, we predict a signed distance for each 3D point—indicating which side of the plane it lies on, and how far away.
This turns plane prediction into a dense prediction problem, allowing us to more accurately anchor the symmetry plane to the scene geometry.
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For more interactive visualizations, check out our project page: https://hanyuc.com/archsym
Huge thanks to the team! Ruojin Cai (@ruojin8), Steve Marschner, and Noah Snavely (@Jimantha).
Come by our poster on Sunday, Poster Session 6, if you’re interested!