3D mesh-based representation of spherical images for dense rotation estimation

Abstract

In this paper, we propose a dense approach for 3D rotation estimation between spherical images, which is simultaneously able to recover the large rotations, robust under clutter and small translations. The key idea is to represent the spherical images by 3D shapes of the triangular mesh surfaces based on image intensity signal. This allows to apply the spherical harmonics representation as 3D shape descriptor. The optimum rotation computation is recovered through the SVD decomposition of the cross covariance matrix, which is obtained from the two 3D shapes spherical harmonics coefficients. The performances of the proposed approach are examined using both synthetic and real image datasets. Experimental results show the effectiveness of our approach for rotation estimation, as well as its robustness against real conditions, image occlusions and small translations. The efficiency of the proposed approach is compared with that of competitive methods.