PHOTOMETRIC AMBIENT OCCLUSION FROM SPARSELY SAMPLED ILLUMINATIONS

Photometric ambient occlusion estimation is to recover the local visibility of a scene from multiple illuminations at a fixed viewpoint. Its effectiveness is highly dependent on the amount of illuminations. In this paper, we study how to reliably extract photometric ambient occlusion from sparsely sampled illuminations. We specifically propose an effective relative entropy minimization framework to encourage the consistency between the data observation and the theoretical distribution. Our extensive results on benchmark datasets show the effectiveness and accuracy of the proposed method. We also demonstrate the improvement of our method in producing high-quality reflectance.