Hierarchical NeRF with JAX3D for Volumetric Rendering, Novel-View Synthesis, and 3D Reconstruction
| Source: MarkTechPost
Tags: NeRF, JAX, jax3d, volumetric rendering, 3D reconstruction, Flax
Step-by-step tutorial for building a hierarchical NeRF with JAX, Flax, and jax3d: covers volumetric rendering, positional encoding, coarse/fine dual networks, hierarchical importance sampling, and novel-view synthesis evaluation including PSNR metrics and marching-cubes 3D geometry extraction.
Details
This tutorial walks through building a hierarchical Neural Radiance Field (NeRF) from scratch using Google's JAX ecosystem and jax3d, a research library from Google Research. The pipeline generates a synthetic multi-view dataset using jax3d's volume rendering primitives (sample_along_rays, volume_rendering), then trains a dual-network NeRF — a coarse network for initial density estimation and a fine network that focuses samples on occupied regions via hierarchical importance sampling using sample_piecewise_constant_pdf. Architecture details: positional encoding maps 3D coordinates and view directions to higher-frequency features; skip connections help gradient flow; separate outputs for color and density. Training uses JAX JIT compilation, Adam optimizer, exponential learning-rate decay, and gradient clipping for stability. Evaluation includes PSNR for quality measurement, depth and opacity visualization, 360-degree turntable rendering, and marching-cubes geometry extraction for dense mesh output. Useful for ML engineers exploring 3D reconstruction or novel-view synthesis workflows in JAX.