This article explains the core principles of Neural Radiance Fields, detailing how a fully‑connected network maps 5‑D coordinates to color and density, the role of positional encoding and hierarchical sampling, and provides a complete PyTorch implementation with training and rendering examples.
NeRF‑Editing introduces an interactive framework that lets users freely deform the geometry of neural radiance fields by coupling an explicit mesh with implicit NeRF representations, propagating mesh vertex changes through tetrahedral ARAP optimization to bend rays during rendering, enabling realistic edits and animations on synthetic and real‑world scenes, a first reported at CVPR 2022.
This tutorial walks through the theory and implementation of Neural Radiance Fields (NeRF) in PyTorch, covering positional encoding, the MLP architecture, differentiable volume rendering, hierarchical sampling, training tricks, and references to the original research.
This article introduces three core 3D volume‑display techniques—triangulation mesh, voxel‑grid, and volume rendering—explaining their principles, advantages, disadvantages, suitable scenarios, and implementation details such as WebGL shaders and instanced drawing for GIS visualisation.
