Much attention is paid to registration of terrestrial point clouds nowadays. Research is carried out towards improved efficiency and automation of the registration process. The most important part of registration is finding correspondence. The panoramic reflectance images are generated according to the angular coordinates and reflectance value of each 3D point of 360° full scans. Since it is similar to a black and white photo, it is possible to implement image matching on this kind of images. Therefore, this paper reports a new corresponding point matching algorithm for panoramic reflectance images. Firstly SIFT (Scale Invariant Feature Transform) method is employed for extracting distinctive invariant features from panoramic images that can be used to perform reliable matching between different views of an object or scene. The correspondences are identified by finding the nearest neighbors of each keypoint form the first image among those in the second image afterwards. The rigid geometric invariance derived from point cloud is used to prune false correspondences. Finally, an iterative process is employed to include more new matches for transformation parameters computation until the computation accuracy reaches predefined accuracy threshold. The approach is tested with panoramic reflectance images (indoor and outdoor scenes) acquired by the laser scanner FARO LS 880.