Image parallax based modeling of depth-layer architecture

Abstract

We present a method to generate a textured 3D model of architecture with a structure of multiple floors and depth layers from image collections. Images are usually used to reconstruct 3D point cloud or analyze facade structure. However, it is still a challenging problem to deal with architecture with depth-layer structure. For example, planar walls and curved roofs appear alternately, front and back layers occlude each other with different depth values, similar materials, and irregular boundaries. A statistic-based top-bottom segmentation algorithm is proposed to divide the 3D point cloud generated by structure-from-motion (SFM) method into different floors. And for each floor with depth layers, a repetition based depth-layer decomposition algorithm based on parallax-shift is proposed to separate the front and back layers, especially for the irregular boundaries. Finally, architecture components are modeled to construct a textured 3D model utilizing the extracting parameters from the segmentation results. Our system has the distinct advantage of producing realistic 3D architecture models with accurate depth information between front and back layers, which is demonstrated by multiple examples in the paper.