In recent years, a demand for 3D models of various scales and precisions 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 pipe-line, mainly focusing on dense stereo-matching which relies on a hierarchical, local optimization scheme. Our matching framework consists of a combination of robust cost measures, extracted via an intuitive cost aggregation support area and set within a coarse-to-fine strategy. The cost function is formulated by combining three individual costs: a cost computed on an extended census transfor-mation of the images; the absolute difference cost, taking into account information from colour channels; and a cost based on the principal image derivatives. An efficient adaptive method of aggregating matching cost for each pixel is then applied, relying on li-nearly expanded cross skeleton support regions. Aggregated cost is smoothed via a 3D Gaussian function. Finally, a simple ‘winner-takes-all’ approach extracts the disparity value with minimum cost. This keeps algorithmic complexity and system computational re-quirements acceptably low for high resolution images (or real-time applications), 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 considera-tions, are presented and evaluated on the Middlebury platform