[Submitted on 8 Sep 2026]
Title:OmniPoint: Universal Monocular Metric Pointcloud from Any Camera
View a PDF of the paper titled OmniPoint: Universal Monocular Metric Pointcloud from Any Camera, by Botao Ye and 3 other authors
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Abstract:Recovering metric 3D geometry from monocular images is a fundamental computer vision task, yet current methods remain heavily fragmented by fixed camera model assumptions and inflexible input schemes. We present OmniPoint, a unified framework designed to generalize metric reconstruction across diverse imaging sensors, including pinhole, fisheye, and equirectangular projections, while accommodating varying geometric priors. To overcome projection rigidity, OmniPoint abandons conventional planar depth regression. It instead adopts a decoupled ray and distance representation alongside a decoupled training objective, explicitly separating the camera projection model from the scene structure. To address the severe scarcity of training data for alternative cameras, we introduce a bidirectional augmentation strategy that explicitly bridges labeled perspective data and unlabeled omnidirectional domains in 3D space. Furthermore, to seamlessly integrate optional inputs like camera intrinsics or sparse depth without destabilizing the network through feature distribution shifts, we propose a robust information injection mechanism. This mechanism utilizes learnable input state embeddings to resolve architectural ambiguity and applies vectorized Gaussian smoothing to densify irregular measurements. Extensive experiments demonstrate that OmniPoint achieves state-of-the-art zero-shot performance across multiple benchmarks, establishing a robust new standard for unified monocular 3D reconstruction.
Comments: ECCV 20026. Project Page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.09394 [cs.CV]
(or arXiv:2609.09394v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.09394
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Botao Ye [view email] [v1] Tue, 8 Sep 2026 19:45:58 UTC (2,093 KB)
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