We present a novel approach on digitizing large scale unstructured environments like archaeological excavations using off-the-shelf digital still cameras. The cameras are calibrated with respect to few markers captured by a theodolite system. Having all cameras registered in the same coordinate system enables a volumetric approach. Our new algorithm has as input multiple calibrated images and outputs an occupancy voxel space where occupied pixels have a local orientation and a confidence value. Both, orientation and confidence facilitate an efficient rendering and texture mapping of the resulting point cloud. Our algorithm combines the following new features: Images are back-projected to hypothesized local patches in the world and correlated on these patches yielding the best orientation. Adjacent cameras build tuples which yield a product of pairwise correlations, called strength. Multiple camera tuples compete each other for the best strength for each voxel. A voxel is regarded as occupied if strength is maximum along the normal. Unlike other multi-camera algorithms using silhouettes, photoconsistency, or global correspondence, our algorithm makes no assumption on camera locations being outside the convex hull of the scene. We present compelling results of outdoors excavation areas.