Methods

Gaussian Splatting

3D Gaussian Splatting for Real-Time Radiance Field Rendering

Authors:
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George Drettakis
Paper:

https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/3d_gaussian_splatting_low.pdf

ID:
gaussian-splatting

Official Gaussian Splatting implementation extended to support distorted camera models. It is fast to train (1 hous) and render (200 FPS).

Instant NGP

Instant Neural Graphics Primitives with a Multiresolution Hash Encoding

Authors:
Thomas Müller, Alex Evans, Christoph Schied, Alexander Keller
Paper:

https://nvlabs.github.io/instant-ngp/assets/mueller2022instant.pdf

Web:

https://nvlabs.github.io/instant-ngp/

ID:
instant-ngp

Instant-NGP is a method that uses hash-grid and a shallow MLP to accelerate training and rendering. This method trains very fast (~6 min) and renders also fast ~3 FPS.

Mip-NeRF 360

Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields

Authors:
Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, Peter Hedman
Paper:

https://arxiv.org/pdf/2111.12077.pdf

Web:

https://jonbarron.info/mipnerf360/

ID:
mipnerf360

Official Mip-NeRF 360 implementation addapted to handle different camera distortion/intrinsic parameters. It was designed for unbounded object-centric 360-degree capture and handles anti-aliasing well. It is, however slower to train and render compared to other approaches.

NerfStudio

Nerfstudio: A Modular Framework for Neural Radiance Field Development

Authors:
Matthew Tancik, Ethan Weber, Evonne Ng, Ruilong Li, Brent Yi, Justin Kerr, Terrance Wang, Alexander Kristoffersen, Jake Austin, Kamyar Salahi, Abhik Ahuja, David McAllister, Angjoo Kanazawa
Paper:

https://arxiv.org/pdf/2302.04264.pdf

Web:

https://docs.nerf.studio/

ID:
nerfacto

NerfStudio (Nerfacto) is a method based on Instant-NGP which combines several improvements from different papers to achieve good quality on real-world scenes captured under normal conditions. It is fast to train (12 min) and render speed is ~1 FPS.

Tetra-NeRF

Tetra-NeRF: Representing Neural Radiance Fields Using Tetrahedra

Authors:
Jonas Kulhanek, Torsten Sattler
Paper:

https://arxiv.org/pdf/2304.09987.pdf

Web:

https://jkulhanek.com/tetra-nerf

ID:
tetra-nerf

Tetra-NeRF is a method that represents the scene as tetrahedral mesh obtained using Delaunay tetrahedralization. The input point cloud has to be provided (for COLMAP datasets the point cloud is automatically extracted). This is the official implementation from the paper.