duckworthd

Thank you :)

I hope so, but it'll be a good while before we can release anything. We have tight dependencies to other not-yet-OSS libraries, and until they're released, our won't work either.

Maybe! That's the seed of a completely different research paper :)

The big difference is access to fisheye lenses a burst mode that can be run for minutes at a time, and the ability to minimize the amount of camera post processing. In principle, the capture could be done with a smartphone, but the experience of doing so is pretty time consuming right now.

You're looking at something called a "neural radiance field" backed by a sparse, low resolution voxel grid and a dense high resolution triplane grid. That's a bit of a word soup, but you can think of it like a glowing fog rendered with ray marching.

The benchmark details are a bit complicated. Check out the technical paper's experiment section for the nitty gritty details.

What's worked well for me: Find a way to put what AI/ML on your critical path. Think of it like learning a new language: classes, lessons, and watching TV helps, but nothing works like full-on immersion. In the context of AI/ML, that means find a way to turn AI/ML into your full-time job or school. It's not easy! But if you do, you'll see endless returns.

If you don't have a solid enough footing to get a job in the field yet, the next best thing in my opinion: find a passion project and keep cooking up new ways to tackle it. On the way to solving your problem, you'll undoubtedly begin absorbing the tools of the trade.

Lastly, consider going back to school (a Bachelor's or Master's, perhaps?). It'll take far more than 1 hour/day, but I promise you, you'll see results far faster and far more concretely than any other learning strategy.

Good luck!

Context: I've been a Researcher/Engineer at Google DeepMind (formerly Google Brain) for the last ~7 years. I studied AI/ML in my BS and MS, but burnt out of a PhD before publishing my first paper. Now I do AI/ML research as a day job.

Yes, I hope so! But it'll take at least a few months of work. We have some tight dependencies to not-yet-open-sourced code, and until that's released, any code we put out will be dead on arrival.

In the meantime, feel free to explore the live viewer code!

https://github.com/smerf-3d/smerf-3d.github.io/blob/main/vie...

This should absolutely be possible! The hard part is making it look natural: NeRF models (including SMERF) have no explicit materials or lighting. That means that any character inserted into the game will look out of place.

Oof, there's a lot of machinery here. It depends a lot on your academic background.

I'd recommend starting with a tutorial on neural radiance fields, aka NeRF, (https://sites.google.com/berkeley.edu/nerf-tutorial/home) and an applied overview of Deep Learning with tools like PyTorch or JAX. This line of work is still "cutting edge" research, so a lot of knowledge hasn't been rolled up into textbook or article form yet.

In terms of the live viewer, there's actually no limit on footprint size. 300 m^2 is simply the biggest indoor capture we had!

Oops, I need to update the license files.

Our code is released under the Apache 2.0 license, as in this repo: https://github.com/google-research/google-research/blob/mast...

The viewer biases towards assets closer to user's camera (otherwise you'd have to load the whole scene!). We tried training SMERF with a larger number of smaller submodels, but at some point, it becomes too onerous to train and quality begins to suffer.

Thanks for the feedback!

I agree, we could do better with the movement UX. A challenge for another day.

You're probably thinking of 3D Gaussian Splatting (3DGS), another fantastic approach to real-time novel view synthesis. There's tons of fantastic work being built on 3DGS right now, and the dust has yet to settle with respect to which method is "better". Right now, I can say that SMERF has slightly higher quality on than 3DGS on small scenes and visibly higher quality on big scenes and runs on a wider variety of devices, but takes much longer than 3DGS to train.

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

Block-NeRF is a predecessor work that helped inspire SMERF, in fact!

https://waymo.com/research/block-nerf/

I hope to release the code in the new year, but we have some big dependencies that need to be released worse. In the meantime, you can already begin hacking on the live viewer, https://github.com/smerf-3d/smerf-3d.github.io/blob/main/vie...

3D understanding as a field is very much in its infancy. Good work is being done in this area, but we've got a long ways to go yet. SMERF is all about "view synthesis" -- rendering realistic images -- with no attempt at semantic understanding or segmentation.

Interesting effect. It does look very voxel-y. I'm not a video game developer at heart, so I can only guess how it was implemented. I doubt NeRF models were involved, but I wouldn't be surprised if some sort of voxel discretization was.

Do you need position data to go along with the photos or just the photos?

Short answer: Yes.

Long answer: Yes, but it can typically be derived from images. Structure-from-motion methods are typically used to derive lens and position information for each photo in the training set. These are then used by Zip-NeRF (our teacher) and SMERF (our model) to train a model.

Update: Code for the web viewer is here,

https://github.com/smerf-3d/smerf-3d.github.io/blob/main/vie...

I can't say. I'm not familiar with BD in Cyberpunk.

I won't say too much about this, but the amount of buzz around articles these days is more of "research today" sort of thing. Top conferences like CVPR receives thousands of submissions each year, and there's a lot of upside to getting your work in front of as many eyeballs as possible.

By no means do I claim that SMERF is the be-all-end-all in real-time rendering, but I do believe it's a sold step in the right direction. There are all kinds of ways to improve this work and others in the field: smaller representation sizes, faster training, higher quality, and fewer input images would all make this technology more accessible.

We unfortunately haven't tested our web viewer in Firefox. Let us know which platform you're running and we'll do our best to take a look in the new year (holiday vacation!).

In the meantime, give it a shot in a Webkit- or Chromium-based browser. I've had good results on Safari on iPhone, Chrome on Android/Macbook/Windows.

I hope to release code in the new year, but it'll take a while. The codebase is heavily wired into other not-yet-open-sourced libraries, and it'll take a while to disentangle them.

Collaboration is a thing at the Big G :)

Be careful with this one! Luma's offering requires that the camera follow the recorded video path. Our method lets the camera go wherever you desire!

Nice discovery :). Check the developer console: it'll tell you.

Thank you!

All these details and more in our technical paper! In short: SMERF training takes much longer, SMERF rendering is nearly as fast as 3DGS when a CUDA GPU is available, and quality is visibly higher than 3DGS on large scenes and slightly higher on smaller scenes.

https://arxiv.org/abs/2312.07541

Not yet, as far as I'm aware. The current flow involves a DSLR for capture, COLMAP for camera parameter estimation, one codebase for training a teacher model, our codebase for training SMERF, and our web viewer for rendering models.

Sounds like an opportunity!