In this tutorial , we build an end-to-end hierarchical Neural Radiance Field (NeRF) using JAX , Flax, Optax, and the volume-rendering primitives provided by jax3d. We first construct a synthetic multi-view dataset from an analytic scene containing volumetric geometry and view-dependent radiance, using sample_along_rays and volume_rendering to establish the forward rendering process. We then implement a NeRF with positional encoding, skip connections, separate coarse and fine networks, and view-direction conditioning, followed by hierarchical importance sampling through sample_piecewise_constant_pdf. We train the model with JAX JIT compilation, Adam optimization, exponential learning-rate decay, and gradient clipping, and finally evaluate novel-view synthesis using PSNR, depth and opacity visualization, sampling diagnostics, 360-degree rendering, and marching-cubes geometry extraction. Copy Code Copied Use a different Browser import os, sys, subprocess, importlib.util, functools, dataclasses, time, math def _sh(cmd): subprocess.run(cmd, shell=True, check=False, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) print("Installing dependencies ...") _sh(f'{sys.executable} -m pip install -q "etils[array-types,epy,etree,enp]" ' f'chex flax optax scikit-image') REPO_DIR = "/content/jax3d" if os.path.isdir("/content") else os.path.abspath("./jax3d") if not os.path.isdir(REPO_DIR): print("Cloning google-research/jax3d ...") _sh(f"git clone -q --depth 1 https://github.com/google-research/jax3d.git {REPO_DIR}") def _load_module_by_path(name, path): """Load a single .py file withou
Source: MarkTechPost