神经镜头建模
Neural Lens Modeling
AI总结：
本文提出 NeuroLens 神经镜头模型，统一建模畸变与暗角，支持点投影和光线投射，可同时用于拍摄前标定和三维重建中的在线标定，并在大规模真实镜头数据集上验证其优于现有方法且易于集成。
摘要：
近年来，三维重建与渲染方法日益受益于对整个图像成像过程的端到端优化。然而，这一方法目前仍存在局限：光学硬件堆栈尤其是镜头的影响难以以统一方式进行建模，这限制了相机标定所能达到的质量以及三维重建结果的保真度。本文提出 NeuroLens，一种用于畸变和暗角的神经镜头模型，可用于点投影和光线投射，并可通过这两种操作进行优化。这意味着它可以（可选地）用于在拍摄前使用经典标定靶进行预标定，之后还可在三维重建过程中进行标定或细化，例如在优化辐射场时使用。为评估所提模型的性能，我们基于 Lensfun 数据库构建了一个包含大量镜头的综合数据集。利用该数据集及其他真实世界数据集，我们表明所提出的镜头模型在质量上优于标准软件包以及近期方法，同时更易于使用和扩展。该模型可泛化到多种镜头类型，并且能够轻松集成到现有的三维重建与渲染系统中。
英文摘要：
Recent methods for 3D reconstruction and rendering increasingly benefit from end-to-end optimization of the entire image formation process. However, this approach is currently limited: effects of the optical hardware stack and in particular lenses are hard to model in a unified way. This limits the quality that can be achieved for camera calibration and the fidelity of the results of 3D reconstruction. In this paper, we propose NeuroLens, a neural lens model for distortion and vignetting that can be used for point projection and ray casting and can be optimized through both operations. This means that it can (optionally) be used to perform pre-capture calibration using classical calibration targets, and can later be used to perform calibration or refinement during 3D reconstruction, e.g., while optimizing a radiance field. To evaluate the performance of our proposed model, we create a comprehensive dataset assembled from the Lensfun database with a multitude of lenses. Using this and other real-world datasets, we show that the quality of our proposed lens model outperforms standard packages as well as recent approaches while being much easier to use and extend. The model generalizes across many lens types and is trivial to integrate into existing 3D reconstruction and rendering systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
