Extending Light Direction Reconstruction to Outdoor and Surface Datasets

Immersive augmented reality (AR) requires consistent illumination between synthetic scene content and the real world. Recent deep neural networks achieve this by reconstructing important illumination parameters, though the performance was measured in clean laboratory environments. This study shows that the reconstruction performance of a recently proposed network architecture remains satisfying in more complex real-world outdoor scenarios. The labelled outdoor scenario dataset used in this study is provided as part of this publication. The study further reveals how auxiliary surface (in this scope, depth and normal vector) information affects the reconstruction performance of the network architecture.