Registration, calibration and blending in creating high quality panoramas

This paper presents a system for creating a full 360degree panorama from rectilinear images captured from a single nodal position. The solution to the problem is divided into three steps. The rst step registers all overlapping images projectively. A combination of a gradient-based optimization method and a correlationbased linear search is found to be robust even in cases of drastic exposure di erences and small amount of parallax. The second step takes the projective matrices and their associated hessian matrices as inputs, and calibrates the internal and external parameters of every images through a global optimization. The objective is to minimize the overall image discrepancies in all overlap regions while converting projective matrices into camera parameters such as focal length, aspect ratio, image center, 3D orientation, etc. The third step re-projects all images onto a panorama by a Laplacian-pyramid-based blending. The purpose of blending is to provide a smooth transition between images and eliminate small residues of misalignments resulting from parallax or imperfect pairwise registrations. The blending masks are generated automatically through the grass re transform. At the end, we brie y explain the necessary human interface for initialization, feedback and manual options.