Materiality maps: A novel scene-based framework for direct multi-view stereovision reconstruction

This paper proposes a novel framework for multi-baseline stereovision exploiting the information redundancy to deal with known problems related to occluded regions. Inputs are multiple images shot or rectified in simplified geometry which induces a convenient sampling scheme of scene space: the disparity space. Instead of uniquely relying on image-space information like most multi-view stereovision methods, we work in this sampled scene space. We use fuzzy visibility reasoning and pixel neighborhood similarity measures in order to optimize fuzzy 3D discrete maps of materiality yielding precise reconstruction even in low texture and semi occluded regions. Our main contribution is to build on the disparity space to propose a new materiality map which locates the object surfaces within the actual scene.