PS 2 F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing.

IF 20.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Bhargav Ghanekar, Vishwanath Saragadam, Dushyant Mehra, Anna-Karin Gustavsson, Aswin C Sankaranarayanan, Ashok Veeraraghavan
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引用次数: 0

Abstract

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.

PS 2 F:用于单镜头三维传感的偏振螺旋点展宽函数。
我们提出了一种依赖于工程点扩散函数(PSF)的紧凑型快照单目深度估计技术。用于显微镜超分辨率成像的传统方法,如双像素 PSF(DHPSF),并不适合比点光源稀疏集更复杂的场景。我们利用克拉梅尔-拉奥下界(Cramér-Rao lower bound)证明,分离 DHPSF 的两个叶片,从而捕捉两个独立的图像,可显著提高深度精度。用于生成 DHPSF 的相位掩模的一个特殊属性是,将相位掩模分成两半会导致两个裂片的空间分离。我们利用这一特性建立了一个基于偏振的紧凑型光学装置,将两个正交线性偏振器分别置于 DHPSF 相位掩模的两半上,然后用偏振敏感相机捕捉生成的图像。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,我们的技术最多可将深度误差降低 50%,而空间分辨率几乎没有损失。
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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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