Hierarchical NeRF with JAX3D for Volumetric Rendering, Novel-View Synthesis, and 3D Reconstruction
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-di
This tutorial describes building a hierarchical Neural Radiance Field, a machine learning technique that can generate 3D reconstructions and create new views of scenes from different angles. The approach uses JAX and related libraries to implement a neural network that learns to render volumetric scenes through a training process involving coarse and fine networks with positional encoding and view-direction conditioning. The technique matters because it enables novel-view synthesis and 3D reconstruction from multi-view data, evaluated through metrics like PSNR and visualizations of depth and geometry. The tutorial provides implementation details including how to set up synthetic datasets, configure training with JAX JIT compilation and gradient optimization, and evaluate results through rendering diagnostics and geometry extraction.
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Also reported by The Verge, SiliconANGLE and MarkTechPost
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Also reported by SiliconANGLE
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