One of the indicators of early lung cancer is a color change in airway mucosa. Bronchoscopy of the major airways can provide high-resolution color video of the airway tree's mucosal surfaces. In addition, 3D MDCT chest images provide 3D structural information of the airways. Unfortunately, the bronchoscopic video contains no explicit 3D structural and position information, and the 3D MDCT data captures no color or textural information of the mucosa. A fusion of the topographical information from the 3D CT data and the color information from the bronchoscopic video, however, enables realistic 3D visualization, navigation, localization, and quantitative color-topographic analysis of the airways. This paper presents a method for topographic airway-mucosal surface mapping from bronchoscopic video onto 3D MDCT endoluminal views. The method uses registered video images and CT-based virtual endoscopic renderings of the airways. The visibility and depth data are also generated by the renderings. Uniform sampling and over-scanning of the visible triangles are done before they are packed into a texture space. The texels are then re-projected onto video images and assigned color values based on depth and illumination data obtained from renderings. The texture map is loaded into the rendering engine to enable real-time navigation through the combined 3D CT surface and bronchoscopic video data. Tests were performed on pre-recorded bronchoscopy patient video and associated 3D MDCT scans. Results show that we can effectively accomplish mapping over a continuous sequence of airway images spanning several generations of airways.