Spherical Visibility Sampling

Download or read book Spherical Visibility Sampling written by Benjamin Eikel and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many 3D scenes (e.g., generated from CAD data) are composed of a multitude of objects that are nested in each other. An industrial plant, for instance, may contain multiple machines and the machines may have an electric motor with many smaller parts like rotor and stator located inside. Since the objects occlude each other, only few are visible from outside. This work presents a new technique, Spherical Visibility Sampling (SVS), for real-time 3D rendering of such -- often highly complex -- scenes. SVS exploits the occlusion and annotates hierarchically structured objects with direction-dependent visibility information in a preprocessing step. For different directions, the direction-dependent visibility encodes which objects of a scene's region are visible from that direction from the outside of the regions' enclosing bounding sphere. Since there is no need to store a separate view space subdivision as in most techniques based on preprocessed visibility, a small memory footprint is achieved. Using the direction-dependent visibility information for an interactive walkthrough, the potentially visible objects can be retrieved very efficiently without the need for further visibility tests. The evaluation shows that using SVS allows to preprocess complex 3D scenes fast and to visualize them in real time (e.g., a Power Plant model and five animated Boeing 777 models with billions of triangles). The comparison with two state-of-the-art occlusion culling algorithms demonstrates the advantages and disadvantages of SVS. Because SVS does not require hardware support for occlusion culling during rendering, it is even applicable for rendering complex scenes on mobile devices. ; eng