Sun position esimation on time-lapse videos for augmented reality applications

Realistic illumination of virtual objects on Augmented Reality (AR) environments is important in terms of achieving visual coherence. This thesis proposes a novel approach that facilitates the illumination estimation on time-lapse videos and gives the opportunity to combine AR technology with time-lapse videos in a visually consistent way. The proposed approach works for both outdoor and indoor environments where the main light source is the Sun. We rst modify an existing illumination estimation method that aims to obtain sparse radiance map of the environment in order to estimate the initial Sun position. We then track the hard ground shadows on the time-lapse video by using an energy-based pixelwise method. The proposed method aims to track the shadows by utilizing the energy values of the pixels that forms them. We tested the method on various time-lapse videos recorded in outdoor and indoor environments and obtained successful results.