Highly parallel steered mixture-of-experts rendering at pixel-level for image and light field data

Abstract

A novel image approximation framework called steered mixture-of-experts (SMoE) was recently presented. SMoE has multiple applications in coding, scale-conversion, and general processing of image modalities. In particular, it has strong potential for coding and streaming higher dimensional image modalities that are necessary to leverage full translational and rotational freedom (6 degrees-of-freedom) in virtual reality for camera captured images. In this paper, we analyze the rendering performance of SMoE for 2D images and 4D light fields. Two different GPU implementations that parallelize the SMoE regression step at pixel-level are presented, including experimental evaluations based on rendering performance and quality. In this paper it is shown that on appropriate hardware, an OpenCL implementation can achieve 85 fps and 22 fps for, respectively, 1080p and 4K renderings of large models with more than 100,000 of Gaussian kernels.

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