极化3D:合成极化和深度线索增强3D传感

A. Kadambi, Vage Taamazyan, Boxin Shi, R. Raskar
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引用次数: 5

摘要

利用偏振线索的形状信息可以增强粗深度图。我们提出了一种极化和深度融合的协同技术,以利用高横向分辨率和补偿传统传感器的低深度分辨率。虽然深度法线和光度法线的融合并不新鲜,但现有的框架无法处理融合偏振法线的独特挑战。我们解决的具体挑战包括方位模糊、折射畸变、正面平行表面和深度不连续。在这个项目中,我们通过一个与其他深度传感器相比更具优势的低成本原型验证了我们的深度增强算法框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Polarized 3D: synthesis of polarization and depth cues for enhanced 3D sensing
Coarse depth maps can be enhanced by using the shape information from polarization cues. We propose a fusion of polarization and depth, a synergistic technique to exploit the high lateral resolution and compensate for the low depth resolution of traditional sensors. Although the fusion of depth and photometric normals is not new, existing frameworks cannot handle the unique challenge of fusing normals from polarization. Specific challenges we address include azimuthal ambiguity, refractive distortion, fronto-parallel surfaces, and depth discontinuities. In this project we validate our algorithmic framework for depth enhancement through a low-cost prototype that compares favorably with other depth sensors.
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