aiqbench takeMachine Learning

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.

Source coveringMarkTechPost

What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Hierarchical NeRF with JAX3D for Volumetric Rendering, Novel-View Synthesis, and 3D Reconstruction

MarkTechPostSeptember 13, 20263 min read

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…

Excerpt shown under the publisher's RSS terms · 62 words · full text and figures are on the publisher's site

Read at MarkTechPost ↗Ask about this article