Recreating the magic of Neural Radiance Fields doesn’t require a black-box library. This tutorial strips away the abstraction to build an end-to-end hierarchical NeRF using JAX, Flax, and Optax. By leveraging volume-rendering primitives from Google Research’s jax3d repository, we construct a synthetic multi-view dataset from an analytic scene. The process begins by establishing a forward rendering pipeline using sample_along_rays and volume_rendering, creating a foundation for view-dependent radiance and volumetric geometry.
The core architecture implements a classic NeRF design but with modern JAX optimizations. It features positional encoding, skip connections, and separate coarse and fine networks conditioned on view direction. Hierarchical importance sampling is handled via sample_piecewise_constant_pdf, allowing the model to focus computational resources on regions with high opacity. Training relies on JAX JIT compilation for speed, Adam optimization, and exponential learning-rate decay. Gradient clipping ensures stability during the 2,500 training steps defined in the default configuration.
Evaluation goes beyond simple loss metrics. The workflow includes novel-view synthesis assessed via PSNR, alongside depth and opacity visualizations. For those interested in 3D reconstruction, the tutorial demonstrates marching-cubes geometry extraction and 360-degree rendering. A key detail for practitioners: the code automatically detects hardware constraints. If no GPU is found, it switches to a CPU-friendly config, reducing image resolution to 40x40 and cutting training steps to 400 to ensure feasibility on weaker hardware.
This approach highlights the modularity of the JAX ecosystem. Instead of relying on monolithic frameworks, developers can mix flax.linen for neural layers with jax3d for geometric math. The explicit handling of camera poses using golden-angle azimuths ensures well-spread views on a dome, a critical factor for high-quality reconstruction. While the synthetic nature of the data simplifies initial debugging, the underlying mechanics mirror production-grade pipelines. It serves as a rigorous testbed for understanding how ray sampling strategies directly impact rendering fidelity.
