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.

In the mirror cup and saucer art created by artists YUL CHO and SANG-HA CHO, part of the saucer is directly visible to the viewer, while the other part of the saucer is occluded and can only be seen as a reflection through a mirror cup. Thus, viewers see an image directly on the saucer and another image on the mirror cup; however, the existing art design is limited to wave-like saucers. In this work, we propose a general computational framework for mirror cup and saucer art design. As input, we take from the user one image for the direct view, one image for the reflected view, and the base shape of the saucer. Our algorithm then generates a suitable saucer shape by deforming the input shape. We formulate this problem as a constrained optimization for the saucer surface. Our framework solves for the fine geometry details on the base shape along with its texture, such that when a mirror cup is placed on the saucer, the user-specified images are observed as direct and reflected views. Through extensive experiments, we demonstrate the effectiveness of our framework and the great design flexibility that it offers to users. We further validate the produced art pieces by fabricating the colored saucers using 3D printing.

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Visual arts refer to art experienced primarily through vision. 3D visual optical art is one of them. Artists use their rich imagination and experience to combine light and objects to give viewers an unforgettable visual experience. However, the design process involves much trial and error; therefore, it is often very time-consuming. This has prompted many researchers to focus on proposing various algorithms to simplify the complicated design processes and help artists quickly realize the arts in their minds. To help computer graphics researchers interested in creating 3D visual optical art, we first classify and review relevant studies, then extract a general framework for solving 3D visual optical art design problems, and finally propose possible directions for future research.

Similarity search is a core analytical task, and its performance critically depends on the choice of distance measure. For time-series querying, elastic measures achieve state-of-the-art accuracy but are computationally expensive. Thus, fast lower bounding (LB) measures prune unnecessary comparisons with elastic distances to accelerate similarity search. Despite decades of attention, there has never been a study to assess the progress in this area. In addition, the research has disproportionately focused on one popular elastic measure, while other accurate measures have received little or no attention. Therefore, there is merit in developing a framework to accumulate knowledge from previously developed LBs and eliminate the notoriously challenging task of designing separate LBs for each elastic measure. In this paper, we perform the first comprehensive study of 11 LBs spanning 5 elastic measures using 128 datasets. We identify four properties that constitute the effectiveness of LBs and propose the Generalized Lower Bounding (GLB) framework to satisfy all desirable properties. GLB creates cache-friendly summaries, adaptively exploits summaries of both query and target time series, and captures boundary distances in an unsupervised manner. GLB outperforms all LBs in speedup (e.g., up to 13.5× faster against the strongest LB in terms of pruning power), establishes new state-of-the-art results for the 5 elastic measures, and provides the first LBs for 2 elastic measures with no known LBs. Overall, GLB enables the effective development of LBs to facilitate fast similarity search.