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NeRF synthesizes novel views of a scene with unprecedented quality by fittinga neural radiance field to RGB images. However, NeRF requires querying a deepMulti-Layer Perceptron (MLP) millions of times, leading to slow renderingtimes, even on modern GPUs. In this paper, we demonstrate that real-timerendering is possible by utilizing thousands of tiny MLPs instead of one singlelarge MLP. In our setting, each individual MLP only needs to represent parts ofthe scene, thus smaller and faster-to-evaluate MLPs can be used. By combiningthis divide-and-conquer strategy with further optimizations, rendering isaccelerated by three orders of magnitude compared to the original NeRF modelwithout incurring high storage costs. Further, using teacher-studentdistillation for training, we show that this speed-up can be achieved withoutsacrificing visual quality.

KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs