JNeRF:An efficient heterogeneous NeRF model zoo based on Jittor

Neural radiance fields(NeRFs)for novel-view synthesis have attracted the attention of researchers in computer vision and graphics.Unlike traditional methods using explicit expressions,NeRFs represent a scene as an implicit neural radiance field.When rendering,NeRF queries the color density at every position in the scene through a neural network.NeRF brings a wide range of possibilities for real-world 3D reconstruction and rendering,but problems remain to be solved.Previous works have improved NeRF’s sampling technique,position encoding method,network structure,etc.,but these improvements are difficult to be combined as the different modules are not well decoupled.Recent works have significantly sped up the core GPU computation of NeRF,leaving the deep learning framework as a major computational cost.Thus,it has been suggested to replace the frameworks by pure CUDA programs,but this limits maintainability and extendability.Therefore,we propose JNeRF,a unified,efficient,framework-friendly NeRF model zoo based on Jittor.