A LOCAL ADAPTIVE APPROACH FOR DENSE STEREO MATCHING IN ARCHITECTURAL SCENE RECONSTRUCTION

摘要

In recent years, a demand for 3D models of various scales and pr ecisions has been growing for a wide range of applications; among them, cultural heritage recording is a particularly important and challenging field. We outline an automatic 3D reconstruction pipeline, mainly focusing on dense stereo-matching which relies on a hierarchical, local optimizati on scheme. Our matching framewor k consists of a combination of robust cost measures, extracted via an intuitive cost aggregation support area and set within a co arse-to-fine strategy. The cost function is formulated by combining th ree individual costs: a cost computed on an extended census trans formation of the images; the absolute difference cost, taking into account information from colour channels; and a cost based on t he principal image derivatives. An efficient adaptive method of aggr egating matching cost for each pixel is then applied, relying on linearly expanded cross skeleton support regions. Aggregated cost is smoothed via a 3D Gaussian function. Finally, a simple 'winn ertakes-all' approach extracts the disparity value with minimum cost. This keeps algorithmic complexity and system computational requirements acceptably low for high resolution images (or real-time a pplications), when compared to complex matching functions of global formulations. The stereo algorithm adopts a hierarchical scheme to accommodate high-resolution images and complex scenes. In a last step, a robust post-processing work-flow is applied to enhance the disparity map and, consequently, the geometric quality of the reconstructed scene. Successful results from our implementation, which combines pre-existing algorithms and novel considerations, are presented and evaluated on the Middlebury platform.