Beyond the Pixel: a Photometrically Calibrated HDR Dataset for Luminance and Color Prediction
This provides a foundational resource for researchers in computer vision and lighting design to enable physically accurate light estimation, addressing a gap in existing datasets.
The authors tackled the lack of physically calibrated datasets for light estimation in computer vision by creating the Laval Photometric Indoor HDR Dataset, which includes photometrically calibrated high dynamic range panoramas, and introduced three novel tasks for predicting luminance, color, and illuminance from images.
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° 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° 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/.