[Submitted on 26 Oct 2026]
Title:Any3R: Feedforward 3D Reconstruction from Any Camera Type
View a PDF of the paper titled Any3R: Feedforward 3D Reconstruction from Any Camera Type, by Jingong Chen and 7 other authors
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Abstract:Geometry foundation models recover camera poses and dense geometry from a set of images with a feed-forward network, but they expect pinhole images. That assumption is inside the backbone too: the positional encoding and the training loss both bind an image position to a direction in space, and both depend on the lens. We condition both on the dense ray field, the unit direction through every pixel, which describes any central camera without a lens type, distortion coefficients or a bound on field of view. Angular coordinates give the rotary encoding the measured ray angle of each patch, evaluated per view, and solid-angle weighting scales the point loss by the area each pixel covers on the sphere, so neither the encoding nor the supervision depends on the lens. Any3R is therefore a feed-forward model that takes any central camera, and is robust to mixing many camera types per scene, with no retraining per lens. On a benchmark of thirteen central cameras across three datasets, where every method reconstructs with its own predicted camera poses, it is the most consistently accurate method. Any3R enables scalable 3D reconstruction from heterogeneous crowdsourced and archival imagery collections sourced across diverse camera types. Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.36150 [cs.CV]
(or arXiv:2610.36150v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.36150
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jingong Chen [view email] [v1] Mon, 26 Oct 2026 18:53:14 UTC (34,283 KB)
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