Cone-Beam Computed Tomography (CBCT) is one of the primary imaging modalities in radiation therapy, dentistry, and orthopedic interventions. While providing crucial intraoperative imaging, CBCT is bounded by its limited imaging volume, motivating the use of image stitching techniques. Current methods rely on overlapping volumes, leading to an excessive amount of radiation exposure, or on external tracking hardware, which may increase the setup complexity. We attach an optical camera to a CBCT enabled C-arm, and co-register the video and X-ray views. Our novel algorithm recovers the spatial alignment of non-overlapping CBCT volumes based on the observed optical views, as well as the laser projection provided by the X-ray system. First, we estimate the transformation between two volumes by automatic detection and matching of natural surface features during the patient motion. Then, we recover 3D information by reconstructing the projection of the positioning-laser onto an unknown curved surface, which enables the estimation of the unknown scale. We present a full evaluation of the methodology, by comparing vision- and registration-based stitching.