> I'm unsure whether it is me, but one of us is confused about the representation of a 2D image with a 3D scene. it's absolutely correct that a digital (2d) image is a grid of pixels. We can call it a soup if you want. An audio file, our text document are soups too.
No, I don't agree that images or audio files or text documents are soups, they're ordered grids or lists of equidistant samples. To be clear, I didn't make up the description triangle soup, the authors did, it's right there in the article.
> A 3D scene (the digital representation) is structure that can't be reduced to a simple grid. At least it better not to or it wouldn't look great from almost all angles.
Yes it can, it's just a matter of resolution. Gaussian splats, Nerfs, and other similar techniques aim to represent the radiance field that the input images are samples of. The radiance field, like the EM field or most other fields, can be quantized to grids, just like how we represent 2d samples of scenes with grids of pixels.
> Gaussian Splatting is a technique designed to tackle what seemed like impossible or at least prove extremely challenging with 3D scene reconstruction.
Gaussian splatting is not used for scene reconstruction in the general sense, it's used for novel view synthesis. It doesn't claim to reconstruct the 3d scene, it only tries to make a estimation of the function that takes a position and direction and returns a color. I think the original Nerf paper does a good job explaining what the radiance field is, and how using images of a scene to estimate it works. 3dgs is a more efficient and intuitive way of doing the same thing.
> I haven't meant anybody who would look at a gaussian splatting reconstruction of a scene, and claim another method would look better. Or even could look better. Maybe some day, but as of 2025 there isn't.
Like I mentioned previosly, they look good and sometimes that's all you need, but as a representation of a 3d scene they're chaotic and not very elegant.
> Imo gaussian splats do not form a voxel.
No they don't.
> Your argument doesn't discuss, compare, or even mention limitations faced by all the traditional mesh-based approaches.
I didn't argue that there are any mesh based methods that look better at the moment.
> All that with the most accurate representation of lighting, reflection. Of whatever the camera was able capture really. Novel inference is just an approximation it doesn't invent anything unless some generative ML is plugged in, faked in, plastered all over so that the word Ai gets mentioned.
3dgs comes up with fake representations of reflections, it pretends that reflective surfaces aren't there and puts splats representing the reflections behind where the reflective surface should be. It does this because it has no concept of scene geometry, all it knows and cares about is optimizing the splats' positions and color so they look like the input images when rendered from the input images' positions.
> I don't think that's me..I think you are confusing the method and parts of the method. Gaussian Splatting is not a technique of generating novel views off some captured data.
Yes, it literally is. From the original paper from 2023 [0]:
We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (≥ 30 fps) novel-view synthesis at 1080p resolution.
Additional Key Words and Phrases: novel view synthesis, radiance fields, 3D gaussians, real-time rendering
Our goal is to optimize a scene representation that allows high quality novel view synthesis, starting from a sparse set of (SfM) points without normals.
> Some would even argue with you: what reality are you talking about. We still don't have a clue what reality is.
It isn't made up of disjoint triangles, I think most people agree with that. And without getting philosophical or diving into atoms or quantum fields, most of reality can be represented as continuous volumes or surfaces at a macro scale.
> All we know is that we seem to perceive things a certain way. Our brain may play a movie in there based on that. .it doesn't matter what's there. perception, then tricking the eyes or our neurons is all we have to focus on to make reconstruction valid.
Yes, if all we care about is novel view synthesis, which is a valid use case.
> Anyhow, there is a lot of confusion out there about gaussian splats. I suspect not many people understand this tech but many are talking loud about it, confusing everyone else.
It's really not that complicated, the original and follow up papers are easy to follow.
[0] https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/
I admire your patience and calmness in addressing just about every point I made, which I so poorly articulated that I concede we could just say I was wrong. Or was simply wrong, not due at all to poor wording.
And thank you for the extra notes surrounding the divergence. I had not read the original paper (again that is) before shooting my personal interpretation, and poor recollection of what the paper said. I should read it again I may not recognize it.
I haven't read the paper you shared yet. I thank you again for your input plus the couple of refs gave me plenty to spend time scratching my head as something must be just wrong with my current understanding.