instant-ngp VS nerf-pytorch

This is really cool! Could you now use something like this to turn the new images in a 3d model? Or even use open pose (controlnet) to generate a bunch of images from different angles and use InstantNeRF to make a 3d model for free!

It is at this point that you should have Instant-NGP setup. The script for the COLMAP processing is in the repo, as well as the steps to perform it. My exact parameters were 3 fps and 16 aabb. It is pretty helpful to add the scripts directory into path for exact access system-wide.

[Project Page] https://nvlabs.github.io/instant-ngp/

instant-ngp ([1]) from NVIDIA can render NeRF in VR in real-time, assuming a very good desktop video card. Note that instant-ngp is not as photo-realistic as Zip-NeRF. But it's still very good!

1. https://github.com/NVlabs/instant-ngp

Playing around with neRF AI (https://github.com/NVlabs/instant-ngp) to create some 3d gear reveals. I think this a fun way to show off a kit, what do you think?

3 not sure if it's the one you want but the -aabb_scale is a crop. This page recommends trying a large value of 128 for some outdoor scenes: https://github.com/NVlabs/instant-ngp/blob/master/docs/nerf_dataset_tips.md

I don't know about lumalabs, but basically all NeRF projects these days are based on NVIDIAs Instant neural graphics primitives ( GitHub: instant-ngp). It utilizes COLMAP for SfM (preprocessing step for the neural network) and runs on average Geforce cards pretty good. The fox example (50 photos) on their page literally takes 5 seconds to complete.

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.