deep-video-mvs VS nerf-pytorch

If deep learning is an option, then you might want to check out http://zak.murez.com/atlas/, https://zju3dv.github.io/neuralrecon/, https://github.com/ardaduz/deep-video-mvs and the references therein. These methods can be better than classical ones, especially if overfitted on a specific type of scene.

Found relevant code at https://github.com/yenchenlin/nerf-pytorch + all code implementations here

I have been curious about NeRF and have tried out the software. I really like the idea, but I have found generating the NeRF representation to be too slow and does not do seem to do too well if the number of cameras in the rig are a bit sparse. I have seen that there have been some evolution and variations of the NeRF concept and I am looking into some of them currently.

While there are other PyTorch implementations out there (e.g., this one and this one), I personally found them somewhat difficult to follow, so I decided to do a complete rewrite of NeRF myself. I tried to stay as close to the authors' text as possible, and I added comments in the code referring back to the relevant sections/equations in the paper. The final result is a tight 374 lines of heavily commented code (320 sloc—"source lines of code"—on GitHub) all contained in a single file. For comparison, this PyTorch implementation has approximately 970 sloc spread across several files, while this PyTorch implementation has approximately 905 sloc. A Colab notebook for the full model can be found here.