1. Automatic Panoramic Image Stitching using Invariant Features
Matthew Brown and David G. Lowe

2. Panoramic Image Stitching
Direct
Feature based
平面，柱面， 球面，其他曲面

3. Model Establish
Assuming that the camera rotates about its optical centre, We parameterise each camera by a rotation vector and focal length .

4. Image Matching
At this stage the objective is to find all matching (i.e.overlapping) images.
Robust Homography Estimation using RANSAC
Probabilistic Model for Image Match Verification

5. Bundle Adjustment
Given a set of geometrically consistent matches between the images, we use bundle adjustment to solve for all of the camera parameters jointly. This is an essential step as concatenation of pairwise homographies would cause accumulated errors and disregard multiple constraints between images, e.g., that the ends of a panorama should join up.

7. Automatic Panorama Straightening
Gain Compensation
Multi-Band Blending

10. Thinks