A Geometric Framework For Vision Modeling In Digital Human Models Using 3D Tessellated Head Scans
The present work deals with the development of a computational geometric framework for vision modeling for performing visibility and legibility analyses in Digital Human Modeling (DHM) using the field-of-view (FoV), estimated geometrically from 3D tessellated head scans. DHM is an inter-disciplinary area of research with the prime objective of evaluating a product, job or environment for intended users through computer-based simulations. Vision modeling in the existing DHM’s has been primarily addressed through FoV modeling using right circular cones (RCC). Perimetry literature establishes that the human FoV is asymmetric due to unrestricted zygomatic vision and restrictions on the nasal side of the face. This observation is neither captured by the simplistic RCC models in DHM, nor rigorously studied in vision literature. Thus, the RCC models for FoV are inadequate for rigorous simulations and the accurate modeling of FoV is required in DHM. The computational framework developed in this work considers three broad components namely, the geometric estimation and representation of FoV, visibility and statistical visibility, and legibility of objects in a given environment. A computational geometric method for estimating FoV from 3D laser-scanned models of the human head is presented in this work. The strong one-to-one similarity between computed and clinically perimetry maps establishes that the FoV can be geometrically computed using tessellated head models, without necessarily going through the conventional interaction based clinical procedures. The algorithm for FoV computation is extended to model the effect of gaze-direction on the FoV resulting in binocular FoV. A novel unit-cube scheme is presented for robust, efficient and accurate modeling of FoV. This scheme is subsequently used to determine the visibility of 3D tessellated objects for a given FoV. In order to carry out population based visibility studies, the statistical modeling FoV and generation of percentile-based FoV curves are introduced for a given population of FoV curves. The percentile data thus generated was not available in the current ergonomics or perimetry literature. Advanced vision analysis involving character-legibility is demonstrated using the unit-cube with an improved measure to incorporate the effect of character-thickness on its legibility.
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