Texture mapping has become a popular tool in the computer graphics industry in the last few years because it is an easy way to achieve a high degree of realism in computer-generated imagery with very little effort. Over the last decade, texturemapping techniques have advanced to the point where it is possible to generate real-time perspective simulations of real-world areas by texture mapping every object surface with texture from photographic images of these real-world areas. The techniques for generating such perspective transformations are variations on traditional texture mapping that in some circles have become known as the Image Perspective Transformation or IPT technology. This article first presents a background survey of traditional texture mapping. It then continues with a description of the texture-mapping variations that achieve these perspective transformations of photographic images of real-world scenes. The style of the presentation is that of a resource survey rather than an in-depth analysis.

This paper describes a novel approach to designing and applying spatial transformations to images, a technique that has only recently been exploited. The concept is to create and save the transformation as a look-up table (LUT) and then to use the look-up table to control the image resampling. This approach is very flexible; it is even amenable to transformations that cannot be implemented using classical approaches.

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In a previous paper1, the authors demonstrated how image warping could be implemented using a pair of two-dimensional (2-D) spatial look-up tables. In that paper, the focus was on generating the tables, not on antialiasing the results. In this paper, the authors present a method for generating resampling kernels from the spatial look-up tables for the purpose of reducing aliasing. The method generates resampling kernels based on the reduction in spatial frequency content that must take place in order to produce an antialiased warped result. Since the reduction in spatial frequency required for each output pixel can be unique, the method is adaptive.