AlignDiff: Learning Physically-Grounded Camera Alignment via Diffusion

• URL: http://arxiv.org/abs/2503.21581v1
• Date: Thu, 27 Mar 2025 14:59:59 GMT
• Title: AlignDiff: Learning Physically-Grounded Camera Alignment via Diffusion
• Authors: Liuyue Xie, Jiancong Guo, Ozan Cakmakci, Andre Araujo, Laszlo A. Jeni, Zhiheng Jia,
• Abstract summary: We introduce a novel framework that addresses camera intrinsic and extrinsic parameters using a generic ray camera model.<n>Unlike previous approaches, AlignDiff shifts focus from semantic to geometric features, enabling more accurate modeling of local distortions.<n>Our experiments demonstrate that the proposed method significantly reduces the angular error of estimated ray bundles by 8.2 degrees and overall calibration accuracy, outperforming existing approaches on challenging, real-world datasets.
• Score: 0.5277756703318045
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate camera calibration is a fundamental task for 3D perception, especially when dealing with real-world, in-the-wild environments where complex optical distortions are common. Existing methods often rely on pre-rectified images or calibration patterns, which limits their applicability and flexibility. In this work, we introduce a novel framework that addresses these challenges by jointly modeling camera intrinsic and extrinsic parameters using a generic ray camera model. Unlike previous approaches, AlignDiff shifts focus from semantic to geometric features, enabling more accurate modeling of local distortions. We propose AlignDiff, a diffusion model conditioned on geometric priors, enabling the simultaneous estimation of camera distortions and scene geometry. To enhance distortion prediction, we incorporate edge-aware attention, focusing the model on geometric features around image edges, rather than semantic content. Furthermore, to enhance generalizability to real-world captures, we incorporate a large database of ray-traced lenses containing over three thousand samples. This database characterizes the distortion inherent in a diverse variety of lens forms. Our experiments demonstrate that the proposed method significantly reduces the angular error of estimated ray bundles by ~8.2 degrees and overall calibration accuracy, outperforming existing approaches on challenging, real-world datasets.

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