MobileNeRF: This approach adapts NeRFs for mobile devices by exploiting the polygon rasterization pipeline for efficient neural field rendering. It achieves very fast rendering times (0.016-0.017s) but requires long training times.
MobileR2L: This method uses a full CNN-based neural light field model with a super-resolution model in its second stage. It achieves real-time inference on mobile devices while maintaining high image quality, rendering a 1008x756 image of real 3D scenes in 18.04ms on an iPhone 13.
Instant NGP (Neural Graphics Primitives): Developed by NVIDIA, this technique significantly speeds up the training and rendering of NeRFs, allowing for near-instantaneous scene reconstruction.
Plenoxels (Plenoptic Voxels): This method replaces neural networks with a sparse 3D grid of spherical harmonics, enabling faster training and competitive quality compared to NeRFs.
NGLOD (Neural Geometric Level of Detail): This approach combines neural implicit representations with explicit geometric representations, allowing for multi-resolution rendering and faster training.
NeRF-MAE (Masked AutoEncoders for NeRFs): This technique applies the concept of masked autoencoders to NeRFs for self-supervised 3D representation learning, potentially improving generalization and efficiency.