Hardware evaluation demonstrates 0.59μJ/pixel neural volume rendering on FPGA, suggesting efficient photorealistic graphics acceleration.
The photorealistic rendering of virtual world has always been a significant challenge in computer graphics. With the advent of Neural Radiance Fields Rendering (NeRF) [1], the past three years has witnessed the explosive development of Neural Volume Rendering (NVR), a data-driven solution to this long-stand problem. However, the scene-representation characteristics of NVR models, coupled with the utilization of Multi-Layer Perceptron (MLP), pose a challenge for the efficient deployment of NVR to existing graphics hardware and dedicated convolutional neural network (CNN) accelerators.
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Yuan et al. (2024) studied this question.
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