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One big difference between NeRFs and this technique is that a NeRF is a jumbled mess of interconnected nodes, while this is a collection of values that lives in 3D space. It's much easier to reason about and could probably be integrated fairly easily in other 3D software like editors and renderers, and probably even animated. I agree that neural nets are cool and useful, but the whole black box function approximator that represents a whole scene at once thing doesn't feel scalable or elegant.

I don't think Nerf as it is now needs to be scalable or elegant. It is research into how far you can neural networks. It has merit in the research itself.

Why doesn’t it feel scalable or elegant.

It’s just a function call and you can always transform the NeRF into a new representation if you want to use traditional 3d software.

How do you edit a NeRF? Let's say I want to change material of an object in the scene, or rotate part of it, or compose several into a new scene, etc. Or animate it. It's not really a good representation of a 3D scene IMO, unless all you care about is novel view synthesis, which is what it was designed to do.

Transforming it to a mesh or SDF is cheating, then it's no longer a NeRF and of course the same cons doesn't apply anymore.

This is still an active area of research. Seems like marching cubes alone are not enough IIRC. Do you have any links I can read for NeRf to USD for example? Saw something pretty good from nvidea a while ago but still not great — still a lot of loss.

I agree that ostracizing researchers using neural networks is not a good path for anyone to go down. However, even though NNs are great at modeling problems…we usually have no idea how those learned models actually work. So NNs are great at solving problems in the short term, but aren’t terribly useful for developing long term solutions due to data requirements and regular retraining.

In this instance, I’m going to choose to take an optimistic stance and believe the authors were talking a friendly elbow jab rather than fueling a mini culture war. The researchers leveraging DNNs are doing great work, but perhaps could be wrangled in just a bit :)

It is incorrect that we don’t understand how NeRFs work. Unlike language models etc., they are not using deep neural networks; they are using shallow multi-layer perceptrons as function optimizers. The behavior of these is well-understood.

> The big difference of course, based on reading this, is that NeRF models the whole light transmission function ("radiance field") whereas this seems to model only the boundary conditions (what the light "hits").

The title of the paper is "3D Gaussian Splatting for Real-Time Radiance Field Rendering". Each rendered pixel weights the contribution of unbounded view-dependent Gaussians. So, no, that's not a difference.

Ah gotcha, thanks for pointing that out.

> I don't see "resisting using neural anything" as a good thing, just because they are popular

I read Aras' quip as more narrowly technical. OFC it's ambiguous and you'd have to ask the man himself.

The gist of NeRF is to obtain an NN representation of a 5D light field (3D position + 2D direction) from samples (photographs) of the real-world light field. Alarm bells ring already- 5 dimensions isn't that many! Considering NeRF has always used a low-rank spherical harmonic representation of the directional domain, it's even more like 3D-and-change. To reconstruct a function of such low dimensionality, why choose an NN?

Then at inference time, for each pixel, you have sample the NN repeatedly over the view ray. This part is exceedingly silly, as compact representations of light fields are a solved bread-and-butter problem in graphics.

Later on Plenoxels explicitly took the "Ne" out of "NeRF", giving far higher training and inference performance (also mentioned ITT). To be fair, and later still, Nvidia somewhat redeemed NNs here with Instant NeRF: https://nvlabs.github.io/instant-ngp/assets/mueller2022insta... ...where the twist was to interpolate fancy input emeddings, which are run through a tiny NN. That tininess is important, as the need to fetch NN weights from VRAM would kick NNs right off the Pareto frontier.

Zooming out, NNs have only seen wide adoption in graphics engineering for reconstruction from sparse data (inc. denoising). Makes sense, as that's a high-dimensional problem. Still, beware that the NN solutions rarely blow handmade algorithms out of the water. I also think using tiny NNs for compression- closely related to reconstruction- has a future too. Beyond that, if NNs were to set the graphics world ablaze, it would've happened by now.

Lots of graphics engineering is just approximating functions, so it's natural NNs have some place here. However, our functions tend to be more understandable, tractable, malleable. It's not an application domain where it's virtually impossible to write an algorithmic solution by hand (let alone one that performs well), like natural language understanding.

Thank you for saying this. I think once things enter the culture war, normal and sensible people will get mugged by stupid, unexamined ideas so long as they’re on the right side of the culture war. So many conversations boil down to AI=BAD or CRYPTO=BAD, which is a shame not because the opposition is wrong - that’s your opinion who am I to disagree - but because once people have their conclusion they just stop thinking and once you stop thinking you’ve got no way of noticing if your thoughts on a matter are totally stupid.

My point is a bit different though. It's not about culture wars or being anti or pro AI. It's about how silly it is to call one optimization function AI and another "not AI" and then make a whole curfuffel about bucking trends, when what you've done is actually something very similar. (Maybe very interesting in its own right and a good solution though, with its own desirable properties.. again, I'm not commenting on the paper itself, just this one off-hand comment in the blog.)

In my view calling a solution "neural" and "AI" because you are optimizing a stack of relus, and then turning around and solving the same (or almost the same) problem by optimizing a bunch of Gaussian model parameters on the same data, and then saying that is "not AI" and "at least it's not neural!" is just a very specious and unnecessary way to delineate things imho. They're just two solutions to the same problem, both data driven, both generating "models".. they have differences, just not differences that I think warranted such a comment in the blog, and moreover I thought it weird to attribute some kind of normative evaluation based on how you categorize the solution.

One thing I will say, is that

AI = means you don't understand how to quantify the problem

AI is a great hack to quickly and unknowingly solve classification problems and other similar things. But you trade massive datasets for explainability. And that's a pretty bitter pill to swallow.

Sounds like you're mixing up AI and deep learning. A* is a type of AI, as is scheduling, sudoku solvers, etc.

No, GP is correct, "AI" as a buzzword means very little at this point.

That's because AI as a buzzword means deep learning now, but there's plenty of other AI that doesn't use training data and requires good knowledge of the problem you want to solve.

If you look at one of the most widely used text books machine learning is one of seven chapters, and not all of that is deep or supervised learning.

https://aima.cs.berkeley.edu/

But as a long-standing (as far as computing is concerned at any rate) academic field of study, it has meaning, even if the precise boundaries aren't perfectly well defined.

That it works without you understanding it is a failure of your understanding not of the approach.

Oh please. Dumb word games isn't going to make the "is this a bird" AI approach explainable. https://xkcd.com/1425/

Even the "simple" AI's with only a few TBs of data for training still have low/no understanding what's going on to identify the features and how they relate. Sure, we can tell this neuron identifies this squiggle, but what does it mean? No fucking clue. It also shows us collectively that most of these "hard" problems we have no clue how to understand them. And although AI allows us to "do it", it's a bad hack for actually understanding, optimizing, and doing.

> That it works without you understanding it is a failure of your understanding not of the approach.

Cool. Then point me towards the tools to explain how weights in a moderate-ly sized NN are created with training data, and associated with each other and how they find the features in question.

Oh yeah. Those tools don't exist.

You seem to find the explanation unsatisfying and are getting mad at the tools. The program that defines how you recognize a bird is defined in the connectivity of the neurons in your eye and your brain. The circuit is the program.

It's not at all clear that there is or even should be a deeper explanation than "a function with sufficiently many parameters can approximate any other function with only a small change to those parameters, and thus allows tractable solving" - whether by gradient descent or some hokey approximation our neurons are probably doing.

We have even less of an idea how our own system of "understanding" works than we do of neural networks "understanding". If you're going to be mad at tools that don't provide satisfying explanations for how they work, then look no further than your own brain. Are you going to give up on thinking, just because when you look at it closely you realize you have no idea how on earth you're even doing it?

There’s no helping the incurious, but there are indeed reams of explorations unto weights and their explanations.

One of my personal favourites is Zeiler and Fergus https://arxiv.org/abs/1311.2901

Tom Zahavy’s “Greying the black box” on deep RL comes to mind too https://arxiv.org/abs/1602.02658

OpenAI has their Microscope tool for investigating individual neurons https://openai.com/research/microscope; more recently OpenAI shared research and tooling leveraging a powerful language model to explain a smaller model’s neurons. https://openai.com/research/language-models-can-explain-neur...

For a true toy box exploration playground.tensorflow.org is worth a play

These may or may not satisfy your curiosities — your perfect tool may not exist…yet. I would argue that the sampling of extant work above are indicative that it and more are possible.