Three CVPR NeRFs walk into one JAX repo

What it does

MultiNeRF is a unified Python/JAX implementation that houses the code for three distinct neural radiance field methods: Mip-NeRF 360, Ref-NeRF, and RawNeRF. Rather than releasing three separate repositories, the authors merged their internal Ref-NeRF and RawNeRF code into their mip-NeRF 360 implementation. The result is a single research environment for training view-synthesis models on posed image datasets and rendering flythrough videos.

The interesting bit

The integration is lopsided in an honest way: the authors state it should exactly reproduce mip-NeRF 360 results, but Ref-NeRF and RawNeRF numbers may drift slightly from their original papers. That transparency is rare. The real labor beneath the hood is the dataset plumbing—extensive camera coordinate conversion helpers, COLMAP integration, and an abstract Dataset class that lets you bring your own posed images without rewriting the training loop.

Key highlights

• Combines three distinct CVPR 2022 NeRF variants in one fork of mip-NeRF.
• Built in JAX, with configurations managed via Gin.
• Ships with loaders for Blender, DTU, Tanks and Temples, and COLMAP-posed LLFF datasets.
• Includes utilities for OpenCV-to-OpenGL pose conversion, scene normalization, and elliptical camera-path generation for video rendering.
• Provides an abstract base class for custom dataloaders if you already have poses from SLAM or other sources.

Caveats

• The authors explicitly label this as research code, not a production framework.
• Exact reproduction of Ref-NeRF and RawNeRF results is not guaranteed, only Mip-NeRF 360.
• Out-of-memory errors are expected on standard hardware unless you reduce batch size and retune the learning rate and iteration count accordingly.

Verdict

Grab this if you are a researcher or graphics hacker looking to compare or extend these three specific NeRF methods. Skip it if you want a polished, batteries-included 3D reconstruction tool that holds your hand through deployment.