As the computational power of toady's devices increases, real-time physically-based rendering becomes possible, and is rapidly gaining attention across a variety of domains. These include gaming, where physically-based rendering enhances immersion and overall entertainment experience, all the way to medicine, where it constitutes a powerful tool for intuitive volumetric data visualization. However, leveraging the obvious benefits of physically-based rendering (also referred to as photo-realistic rendering) remains challenging on embedded devices such as optical see-through head-mounted displays because of their limited computational power, and restricted memory usage and power consumption. We propose methods that aim at overcoming these limitations, fueling the implementation of real-time physically-based rendering on embedded devices. We navigate the compromise between memory requirement, computational power, and image quality to achieve reasonable rendering results by introducing a flexible representation of plenoptic functions and adapting a fast approximation algorithm for image generation from our plenoptic functions. We conclude by discussing potential applications and limitations of the proposed method.
Several others followed 's considerations and investigated different geometrical primitives for parameterization. @cite_17 examined a sphere as primitive to provide more uniformly sampled light fields. They perform a binning approach based on a Bresenham-style discretization of the spherical surface which introduces two drawbacks: First, this sampling scheme is not perfectly uniform, and second, retrieving radiance information back from the data structure has linear time complexity and is dependent on the number of bins. This suggests that rendering time substantially increases with higher resolution, since both the number of rays as well as the retrieval time per ray increases.
@cite_17
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Image-based or light field rendering has received much recent attention as an alternative to traditional geometric methods for modeling and rendering complex objects. A light field represents the radiance flowing through all the points in a scene in all possible directions. We explore two new techniques for efficiently acquiring, storing, and reconstructing light fields in a (nearly) uniform fashion. Both techniques sample the light field by sampling the set of lines that intersect a sphere tightly fit around a given object. Our first approach relies on uniformly subdividing the sphere and representing this subdivision in a compact data structure which allows efficient mapping of image pixels or rays to sphere points and then to subdivision elements. We sample a light field by joining pairs of subdivision elements and store the resulting samples in a multi-resolution, highly compressed fashion that allows efficient rendering. Our second method allows a uniform sampling of all five dimensions of the light field, using hierarchical subdivision for directional space and uniform grid sampling for positional space. Light field models are acquired using parallel projections along a set of uniform directions. Depth information can also be stored for high-quality image rendering. The system can provide bounds on key sources of error in the representation and can be generalized to arbitrary scenes comprising multiple complex objects.