View Management of Projected Labels on Nonplanar and Textured Surfaces

This paper presents a new label layout technique for projection-based augmented reality (AR) that determines the placement of each label directly projected onto an associated physical object with a surface that is normally inappropriate for projection (i.e., nonplanar and textured). Central to our technique is a new legibility estimation method that evaluates how easily people can read projected characters from arbitrary viewpoints. The estimation method relies on the results of a psychophysical study that we conducted to investigate the legibility of projected characters on various types of surfaces that deform their shapes, decrease their contrasts, or cast shadows on them. Our technique computes a label layout by minimizing the energy function using a genetic algorithm (GA). The terms in the function quantitatively evaluate different aspects of the layout quality. Conventional label layout solvers evaluate anchor regions and leader lines. In addition to these evaluations, we design our energy function to deal with the following unique factors, which are inherent in projection-based AR applications: the estimated legibility value and the disconnection of the projected leader line. The results of our subjective experiment showed that the proposed technique could significantly improve the projected label layout.