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This tutorial details the implementation of a hierarchical Neural Radiance Field (NeRF) using JAX and JAX3D for novel-view synthesis and 3D reconstruction. The approach leverages volume-rendering primitives, positional encoding, and hierarchical importance sampling to train an end-to-end model on synthetic multi-view data. It demonstrates practical application of machine learning techniques in volumetric geometry and view-dependent radiance modeling.
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 […] The post Hierarchical NeRF with JAX3D for Volumetric Rendering, Novel-View Synthesis, and 3D Reconstruction appeared first on MarkTechPost.
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…
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