Uniformly sampling multi-resolution analysis for image-based relighting

Image-based relighting allows us to efficiently light a scene under complicated illumination conditions. However, the traditional cubemap based multi-resolution analysis unevenly samples the spherical surface, with a higher sampling rate near the face corners and a lower one near the face centers. The non-uniformity penalizes the efficiency of data representation. This paper presents a uniformly sampling multi-resolution analysis approach, namely the icosahedron spherical wavelets (ISW), for image-based relighting under time-varying distant environment. Since the proposed ISW approach provides a highly uniform sampling distribution over the spherical domain, we thus can efficiently handle high frequency variations locally in the illumination changes as well as reduce the number of wavelet coefficients needed in the renderings. Furthermore, visual artifacts are demonstrated to be better suppressed in the proposed ISW approach. Compared with the traditional cubemap based multi-resolution analysis approach, we show that our approach can effectively produce higher quality image sequences that are closer to the ground truth in terms of percentage square errors.