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 introduce the Laval Photometric Indoor HDR Dataset, the first large-scale photometrically calibrated dataset of high dynamic range 360{\deg} 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, and varied types of light sources. We exploit the dataset to introduce three novel tasks, where: per-pixel luminance, per-pixel color and planar illuminance can be predicted from a single input image. Finally, we also capture another smaller photometric dataset with a commercial 360{\deg} 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. Dataset and code are available at https://lvsn.github.io/beyondthepixel/.

翻译：光对人类福祉具有重要影响。然而，大多数计算机视觉任务仅处理像素，而未考虑其与物理亮度的关联。为弥补这一缺陷，我们提出了Laval Photometric Indoor HDR数据集，这是首个大规模光度校准的高动态范围360°全景数据集。我们的核心贡献在于对现有未校准HDR数据集进行校准。具体方法为：在多种光照条件下，同步使用专业光度测量设备（色度计）精确捕捉RAW包围曝光图像，并针对多个场景进行测量。基于获得的测量数据，我们建立了应用于HDR图像的校准系数。最终生成的数据集全面呈现了室内场景，涵盖广泛的照度范围、色彩变化及多样化的光源类型。我们利用该数据集提出了三项全新任务：从单张输入图像中逐像素预测亮度、逐像素预测颜色，以及平面照度预测。此外，我们还使用商用360°相机采集了另一个较小规模的光度数据集，以验证方法在不同相机间的泛化能力。我们相信，该数据集及配套代码的发布将激发学界对物理精确光照估计的研究兴趣。数据集与代码已发布于https://lvsn.github.io/beyondthepixel/。