Smooth Assembled Mappings for Large-Scale Real Walking

Virtual reality applications prefer real walking to provide highly immersive presence than other locomotive methods. Mapping-based techniques are very effective for supporting real walking in small physical workspaces while exploring large virtual scenes. However, the existing methods for computing real walking maps suffer from poor quality due to distortion. In this paper, we present a novel divide-and-conquer method, called Smooth Assembly Mapping (SAM), to compute real walking maps with low isometric distortion for large-scale virtual scenes. First, the input virtual scene is decomposed into a set of smaller local patches. Then, a group of local patches is mapped together into a real workspace by minimizing a low isometric distortion energy with smoothness constraints between the adjacent patches. All local patches are mapped and assembled one by one to obtain a complete map. Finally, a global optimization is adopted to further reduce the distortion throughout the entire map. Our method easily handles teleportation technique by computing maps of individual regions and assembling them with teleporter conformity constraints. A large number of experiments, including formative user studies and comparisons, have shown that our method succeeds in generating high-quality real walking maps from large-scale virtual scenes to small real workspaces and is demonstrably superior to state-of-the-art methods.