We propose a 2D registration method for multi-modal image sequences of the retinal fundus, and a 3D metric reconstruction of near planar surface from multiple views. There are two major contributions in our paper. For 2D registration, our method produces high registration rates while accounting for large modality differences. Compared with the state of the art method [5], our approach has higher registration rate (97.2% vs. 82.31%) while the computation time is much less. This is achieved by extracting features from the edge maps of the contrast enhanced images, and performing pairwise registration by matching the features in an iterative manner, maximizing the number of matches and estimating homographies accurately. The pairwise registration result is further globally optimized by an indirect registration process. For 3D registration part, images are registered to the reference frame by transforming points via a reconstructed 3D surface. The challenge is the reconstruction of a near planar surface, in which the shallow depth makes it a quasi-degenerate case for estimating the geometry from images. Our contribution is the proposed 4-pass bundle adjustment method that gives optimal estimation of all camera poses. With accurate camera poses, the 3D surface can be reconstructed using the images associated with the cameras with the largest baseline. Compared with state of the art 3D retinal image registration methods, our approach produces better results in all image sets.