Photorealistic rendering of real-world scenes is a tremendous challenge witha wide range of applications, including MR (Mixed Reality), and VR (MixedReality). Neural networks, which have long been investigated in the context ofsolving differential equations, have previously been introduced as implicitrepresentations for Photorealistic rendering. However, realistic renderingusing classic computing is challenging because it requires time-consumingoptical ray marching, and suffer computational bottlenecks due to the curse ofdimensionality. In this paper, we propose Quantum Radiance Fields (QRF), whichintegrate the quantum circuit, quantum activation function, and quantum volumerendering for implicit scene representation. The results indicate that QRF notonly takes advantage of the merits of quantum computing technology such as highspeed, fast convergence, and high parallelism, but also ensure high quality ofvolume rendering.
通过quantum techniques加速nerf.
@inproceedings{QRF,title={QRF: Implicit Neural Representations with Quantum Radiance Fields},author={Yang, YuanFu and Sun, Min},year={2022},tags={nerf-speed},sida={通过quantum techniques加速nerf.},}
