用于单镜头三维传感的偏振螺旋点扩展函数

IF 20.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2022-07-03 DOI:10.48550/arXiv.2207.00945
B. Ghanekar, Vishwanath Saragadam, Dushyant Mehra, A. Gustavsson, Aswin C. Sankaranarayanan, A. Veeraraghavan
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引用次数: 2

摘要

我们提出了一种基于工程点扩散函数(PSF)的紧凑快照单目深度估计技术。在微观超分辨率成像中使用的传统方法,如双螺旋PSF(DHPSF),不适合于比稀疏的一组点光源更复杂的场景。我们使用Cramér-Rao下界表明,分离DHPSF的两个波瓣,从而捕获两个单独的图像,可以显著提高深度精度。用于生成DHPSF的相位掩模的一个特殊性质是,将相位掩模分离为两半导致两个波瓣的空间分离。我们利用这一特性构建了一个紧凑的基于偏振的光学设置,在DHPSF相位掩模的每一半上放置两个正交线性偏振器,然后用偏振敏感相机捕捉得到的图像。模拟和实验室原型的结果表明,与包括DHPSF和Tetrapod PSF在内的最先进设计相比,我们的技术实现了高达50%的深度误差降低,空间分辨率几乎没有损失。
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PS2F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing
We propose a compact snapshot monocular depth estimation technique that relies on an engineered point spread function (PSF). Traditional approaches used in microscopic super-resolution imaging such as the Double-Helix PSF (DHPSF) are ill-suited for scenes that are more complex than a sparse set of point light sources. We show, using the Cramér-Rao lower bound, that separating the two lobes of the DHPSF and thereby capturing two separate images leads to a dramatic increase in depth accuracy. A special property of the phase mask used for generating the DHPSF is that a separation of the phase mask into two halves leads to a spatial separation of the two lobes. We leverage this property to build a compact polarization-based optical setup, where we place two orthogonal linear polarizers on each half of the DHPSF phase mask and then capture the resulting image with a polarization-sensitive camera. Results from simulations and a lab prototype demonstrate that our technique achieves up to 50% lower depth error compared to state-of-the-art designs including the DHPSF and the Tetrapod PSF, with little to no loss in spatial resolution.
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来源期刊
CiteScore
28.40
自引率
3.00%
发文量
885
审稿时长
8.5 months
期刊介绍: The IEEE Transactions on Pattern Analysis and Machine Intelligence publishes articles on all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence, with a particular emphasis on machine learning for pattern analysis. Areas such as techniques for visual search, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition and relevant specialized hardware and/or software architectures are also covered.
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