Light plays an important role in human well-being. However, most computer
vision tasks treat pixels without considering their relationship to physical
luminance. To address this shortcoming, we present the first large-scale
photometrically calibrated dataset of high dynamic range \ang{360} panoramas.
Our key contribution is the calibration of an existing, uncalibrated HDR
Dataset. We do so by accurately capturing RAW bracketed exposures
simultaneously with a professional photometric measurement device (chroma
meter) for multiple scenes across a variety of lighting conditions. Using the
resulting measurements, we establish the calibration coefficients to be applied
to the HDR images. The resulting dataset is a rich representation of indoor
scenes which displays a wide range of illuminance and color temperature, and
varied types of light sources. We exploit the dataset to introduce three novel
tasks: where per-pixel luminance, per-pixel temperature and planar illuminance
can be predicted from a single input image. Finally, we also capture another
smaller calibrated dataset with a commercial \ang{360} camera, to experiment on
generalization across cameras. We are optimistic that the release of our
datasets and associated code will spark interest in physically accurate light
estimation within the community.

介绍了一种利用光度计经过校准的高动态范围全景图数据集的方法，可用于在计算机视觉任务中更准确地估计室内场景中的像素亮度、颜色温度和照明情况。

超越像素：一个光度校准的 HDR 数据集，用于预测亮度和色温

Beyond the Pixel: a Photometrically Calibrated HDR Dataset for Luminance  and Color Temperature Prediction

We review the AIM 2020 challenge on virtual image relighting and illumination
estimation. This paper presents the novel VIDIT dataset used in the challenge
and the different proposed solutions and final evaluation results over the 3
challenge tracks. The first track considered one-to-one relighting; the
objective was to relight an input photo of a scene with a different color
temperature and illuminant orientation (i.e., light source position). The goal
of the second track was to estimate illumination settings, namely the color
temperature and orientation, from a given image. Lastly, the third track dealt
with any-to-any relighting, thus a generalization of the first track. The
target color temperature and orientation, rather than being pre-determined, are
instead given by a guide image. Participants were allowed to make use of their
track 1 and 2 solutions for track 3. The tracks had 94, 52, and 56 registered
participants, respectively, leading to 20 confirmed submissions in the final
competition stage.

本文回顾了 AIM 2020 挑战赛中有关虚拟图像照明和光照估计的部分，介绍了在此挑战中使用的新型 VIDIT 数据集以及展示的不同解决方案和每个赛道的最终评估结果。