The recent development of the on-chip micro-polarizer technology has made it
possible to acquire four spatially aligned and temporally synchronized
polarization images with the same ease of operation as a conventional camera.
In this paper, we investigate the use of this sensor technology in
high-dynamic-range (HDR) imaging. Specifically, observing that natural light
can be attenuated differently by varying the orientation of the polarization
filter, we treat the multiple images captured by the polarization camera as a
set captured under different exposure times. In our approach, we first study
the relationship among polarizer orientation, degree and angle of polarization
of light to the exposure time of a pixel in the polarization image.
Subsequently, we propose a deep snapshot HDR reconstruction framework to
recover an HDR image using the polarization images. A polarized HDR dataset is
created to train and evaluate our approach. We demonstrate that our approach
performs favorably against state-of-the-art HDR reconstruction algorithms.

本研究探讨了利用微型极化器技术进行高动态范围成像，通过对不同极化滤镜角度获取的多张图像进行不同曝光时间的拟合，最终提出了一种基于深度快照的 HDR 重建算法，并创建了数据集进行验证，结果表明该方法超越了现有最先进的 HDR 重建算法。

基于偏振相机的深度快照 HDR 重建

Deep Snapshot HDR Reconstruction Based on the Polarization Camera

This paper tackles the problem of estimating 3D body shape of clothed humans
from single polarized 2D images, i.e. polarization images. Polarization images
are known to be able to capture polarized reflected lights that preserve rich
geometric cues of an object, which has motivated its recent applications in
reconstructing surface normal of the objects of interest. Inspired by the
recent advances in human shape estimation from single color images, in this
paper, we attempt at estimating human body shapes by leveraging the geometric
cues from single polarization images. A dedicated two-stage deep learning
approach, SfP, is proposed: given a polarization image, stage one aims at
inferring the fined-detailed body surface normal; stage two gears to
reconstruct the 3D body shape of clothing details. Empirical evaluations on a
synthetic dataset (SURREAL) as well as a real-world dataset (PHSPD) demonstrate
the qualitative and quantitative performance of our approach in estimating
human poses and shapes. This indicates polarization camera is a promising
alternative to the more conventional color or depth imaging for human shape
estimation. Further, normal maps inferred from polarization imaging play a
significant role in accurately recovering the body shapes of clothed people.

该研究论文提出了一种针对服装人体的三维形状估计方法，通过使用极化图像捕获几何线索来重构人体表面法线图，并采用深度学习模型将其转化为 3D 人体形态估计，其结果表明极化相机对于人体形态估计是一种有前途的替代方法。