  Novel view synthesis is an important problem with many applications,
including AR/VR, gaming, and simulations for robotics. With the recent rapid
development of Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS)
methods, it is becoming difficult to keep track of the current state of the art
(SoTA) due to methods using different evaluation protocols, codebases being
difficult to install and use, and methods not generalizing well to novel 3D
scenes. Our experiments support this claim by showing that tiny differences in
evaluation protocols of various methods can lead to inconsistent reported
metrics. To address these issues, we propose a framework called NerfBaselines,
which simplifies the installation of various methods, provides consistent
benchmarking tools, and ensures reproducibility. We validate our implementation
experimentally by reproducing numbers reported in the original papers. To
further improve the accessibility, we release a web platform where commonly
used methods are compared on standard benchmarks. Web:
https://jkulhanek.com/nerfbaselines
