232 karma · joined December 11, 2020
SOGS compression keeps coming up here as a go-to method for reducing Gaussian Splatting model sizes. I put together a deep dive into how SOGS actually works under the hood, with some practical insights on how it can be used in production.
The article stays fairly high-level, but I'm happy to dive into specifics in the comments. I learned quite a bit from implementing my own version of SOGS compression.
You can't get a consumer-grade GPU with enough VRAM to run a large model, but you can do so with macbooks.
I wonder if doubling down on that and shipping devices that let you run third party AI models locally and privately will be their path.
If only they made their unified memory faster as that seems to be the biggest bottleneck regarding LLMs and their tk/s performance.
The key goal is that the creators of 3DGS models can use Blurry as a powerful tool to build the 3D experience that is performant, simple, and aesthetically pleasant for end users (viewers).
3DGS models can be shared via a link or embedded on a website, notion, etc..
Link: https://useblurry.com
Abstracting all of this complexity away in one general tool/library and pretending that it will always work is snake oil. There are no shortcuts to building truly high quality product at a large scale.
I disagree that this is the accurate way to think about LLMs. LLMs still use a finite number of parameters to encode the training data. The amount of training data is massive in comparison to the number of parameters LLMs use, so they need to be somewhat capable of distilling that information into small pieces of knowledge they can then reuse to piece together the full answer.
But this being said, they are not capable of producing an answer outside of the training set distribution, and inherit all the biases of the training data as that's what they are trying to replicate.
> I guess my point is, when you use LLMs for tasks, you're getting whatever other humans have said. And I've seen some pretty poor code examples out there. Yup, exactly this.
More formats are definitely on the roadmap. At first I'd like to add full support for higher order of spherical harmonics and then I wanted to take a look at other methods, too. Maybe 2DGS and/or SMERF would make sense for comparison purposes.
Explanation of controls makes sense, too. I'll add that.
Regarding WebXR, that's currently not on my roadmap. However, if the demand is there I'd consider adding it
- Adding better controls (and an explanation) makes sense!
- Good job discovering the L control. That's actually something that's not supposed to be public. I use that tool to find the correct orientation of splats before publishing them. But once I find those values saved in the DB, there's no use for that tool anymore. When I make it available to the users I'll defo make the UX clearer.
- Good point about landscape mode. I'll fix that.
One of my future plans is to extend it to other methods than just gaussian splatting so that we can compare methods across a wide range of captures with varying light conditions, different materials, etc..
I think SplatGallery is an exploration of how people use the technology and what can it be useful for today and what is the next step.
Btw, I am happy you love the music! It's royalty free music I added there, but I should add some credits as it seems many people listen to it. Since I published this last week it used up all my 100GB Vercel allowance and I had to move it to Google Storage.
And thanks for the bug report. I'll take a look at it this weekend!
And yes, you are right, Upwork can be a race to the bottom. Since I am based in a rather expensive city, I can't really compete with other developers on price, but I can still compete on offering, expertise, commitment, etc.. So far I managed to find enough clients who are willing to pay extra for a premium service.
Naturally, it also depends on the service you are providing. Luckily, my niche is not as saturated yet.
The other point with companies not willing to hire an individual is something I can relate to as well. And not only in regulated industries. It's mostly startups and small companies I work with at the moment.
I find jobs on Upwork and I do cold reach out on LinkedIn and via email. So far, I managed to keep myself pretty busy and I landed a few pretty good projects. I consider that a success since I went into the freelancing world not knowing much about it.
It's hard, especially with the uncertainty of where the next project/money is coming from, but I can't imagine going back to a full-time employment anytime soon.
I'm a full stack software engineer. I have hands-on experience with machine-learning, especially computer vision and LLMs. I specialise in building websites and web experiences end-to-end also with the design, basically the whole package. But I am happy to jump onto ongoing project, too!
Any idea you might have, I'm happy to talk about it and I'll help you bring it to life :)
Tech stack of my choice: Python, Typescript, Next.js, TailwindCSS;
Currently, I am mostly looking for projects or contract work.
Website: https://www.martinpiala.com email: hello@martinpiala.com
Just out of curiosity, is a React client or even a javascript library on your roadmap?
I see you link a jira ticket to a page. Is there a way to link whole Epics to Acreom page?
However, it gets interesting when you realise that if writing the code with an LLM is dirt cheap, then 1000 iterations of writing the same code with the guidance of a skilled software engineer would still be cheap and probably faster. I can imagine a world where whole engineering teams are replaced by just one engineer with a code-generating LLM.
I have not tried the open source LLMs so far as there's the additional hassle renting a server and deploying it there. Running it locally on a consumer GPU does not cut it yet as it is too slow. So to iterate faster, I prefer just using ChatGPT.