In this paper, we propose an efficient computational method for converting local coordinates to world coordinates using specially structured coordinate data. The problem in question is the computation of world coordinates of an object throughout a motion, assuming that we only know the changing coordinates of some fixed surrounding reference points in the local coordinate system of the object. The proposed method is based on barycentric coordinates; by taking the aforementioned static positions as the vertices of a polyhedron, we can specify the coordinates of the object in each step with the help of barycentric coordinates. This approach can significantly help us to achieve more accurate results than by using other possible methods. In the paper, we describe the problem and barycentric coordinate-based solution in detail. We then compare the barycentric method with a technique based on transformation matrices, which we also tested for solving our problem. We also present various diagrams that demonstrate the efficiency of our proposed approach in terms of precision and performance.

Visual aberrations are the imperfections in human vision, which play an important role in our everyday lives. Existing algorithms to simulate such conditions are either not suited for low‐latency workloads or limit the kinds of supported aberrations. In this paper, we present a new simulation method that supports arbitrary visual aberrations and runs at interactive, near real‐time performance on commodity hardware. Furthermore, our method only requires a single set of on‐axis phase aberration coefficients as input and handles the dynamic change of pupil size and focus distance at runtime. We first describe a custom parametric eye model and parameter estimation method to find the physical properties of the simulated eye. Next, we talk about our parameter sampling strategy which we use with the estimated eye model to establish a coarse point‐spread function (PSF) grid. We also propose a GPU‐based interpolation scheme for the kernel grid which we use at runtime to obtain the final vision simulation by extending an existing tile‐based convolution approach. We showcase the capabilities of our eye estimation and rendering processes using several different eye conditions and provide the corresponding performance metrics to demonstrate the applicability of our method for interactive environments.