Adaptive Enhancement of 3D Scenes using Hierarchical Registration of Texture-Mapped 3D models

Adaptive fusion of new information in a 3D urban scene is an important goal to achieve in computer vision, graphics, and visualization. In this work we acquire new image pairs of a scene from closer distances and extract 3D models of successively higher resolutions. We present a new hierarchical approach to register these texture-mapped 3D models with a coarse 3D texture mapped model of an urban scene. First, we use the standard reconstruction algorithm to construct 3D models after establishing 1-1 correspondence between the feature points of two images at same resolution. Next, a subset of these feature points is used to register the higher resolution image with the lower resolution image using a scale-sensitive algorithm. Finally we register and consistently merge the 3D models at different resolutions. We present the results of our hierarchical algorithm for adaptive enhancement of a mural inside the UCSC Campus by registering data that differ in scale by a ratio of 1:15. Results indicate that the proposed hierarchical registration technique effectively utilizes the intermediate models to enable the smooth registration of the high resolution models on the coarser models.