Comment by tel

21 days ago

Gaussian splatting is a way to record 3-dimensional video. You capture a scene from many angles simultaneously and then combine all of those into a single representation. Ideally, that representation is good enough that you can then, post-production, simulate camera angles you didn't originally record.

For example, the camera orbits around the performers in this music video are difficult to imagine in real space. Even if you could pull it off using robotic motion control arms, it would require that the entire choreography is fixed in place before filming. This video clearly takes advantage of being able to direct whatever camera motion the artist wanted in the 3d virtual space of the final composed scene.

To do this, the representation needs to estimate the radiance field, i.e. the amount and color of light visible at every point in your 3d volume, viewed from every angle. It's not possible to do this at high resolution by breaking that space up into voxels, those scale badly, O(n^3). You could attempt to guess at some mesh geometry and paint textures on to it compatible with the camera views, but that's difficult to automate.

Gaussian splatting estimates these radiance fields by assuming that the radiance is build from millions of fuzzy, colored balls positioned, stretched, and rotated in space. These are the Gaussian splats.

Once you have that representation, constructing a novel camera angle is as simple as positioning and angling your virtual camera and then recording the colors and positions of all the splats that are visible.

It turns out that this approach is pretty amenable to techniques similar to modern deep learning. You basically train the positions/shapes/rotations of the splats via gradient descent. It's mostly been explored in research labs but lately production-oriented tools have been built for popular 3d motion graphics tools like Houdini, making it more available.

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tel

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pleurotus  20 days ago

Thanks for the explanation! It makes a lot of sense that voxels would scale as badly as they do, especially if you want to increase resolution. Am I right in assuming that the reason this scales a lot better is because the Gaussian splats, once there's enough "resolution" of them, can provide the estimates for how light works reasonably well at most distances? What I'm getting at is, if I can see Gaussian splats vs voxels similarly to pixels vs vector graphics in images?

  • tel  20 days ago

    I think, yes, with greater splat density—and, critically, more and better inputs to train on, others have stated that these performances were captured with 56 RealSense D455fs—then splats will more accurately estimate light at more angles and distances. I think it's likely that during capture they had to make some choices about lighting and bake those in, so you might still run into issues matching lighting to your shots, but still.

    https://www.realsenseai.com/products/real-sense-depth-camera...

    That said, I don't think splats:voxels as pixels:vector graphics. Maybe a closer analogy would be pixels:vectors is the same as voxels:3d mesh modeling. You might imagine a sophisticated animated character being created and then animated using motion capture techniques.

    But notice where these things fall apart, too. SVG shines when it's not just estimating the true form, but literally is it (fonts, simplified graphics made from simple strokes). If you try to estimate a photo using SVG it tends to get messy. Similar problems arise when reconstructing a 3d mesh from real-world data.

    I agree that splats are a bit like pixels, though. They're samples of color and light in 3d (2d) space. They represent the source more faithfully when they're more densely sampled.

    The difference is that a splat is sampled irregularly, just where it's needed within the scene. That makes it more efficient at representing most useful 3d scenes (i.e., ones where there are a few subjects and objects in mostly empty space). It just uses data where that data has an impact.

MITSardine  20 days ago

Are meshes not used instead of gaussian splats only due to robustness reasons? I.e., if there were a piece of software that could reliably turn a colored point cloud into a textured mesh, would that be preferable?

  • corysama  20 days ago

    Photogrammetry has been around for a long time now. It uses pretty much the same inputs to create meshes from a collection of images of a scene.

    It works well for what it does. But, it's mostly only effective for opaque, diffuse, solid surfaces. It can't handle transparency, reflection or "fuzz". Capturing material response is possible, but requires expensive setups.

    A scene like this poodle https://superspl.at/view?id=6d4b84d3 or this bee https://superspl.at/view?id=cf6ac78e would be pretty much impossible with photogrammetry and very difficult with manual, traditional, polygon workflows. Those are not videos. Spin them around.

  • dahart  20 days ago

    It’s not only for robustness. Splats are volumetric and don’t have topology constraints, and both of those things are desirable. The volume capability is sometimes used for volume effects like fog and clouds, but it also gives splats a very graceful way to handle high frequency geometry - higher frequency detail than the capture resolution - that mesh photogrammetry can’t handle (hair, fur, grass, foliage, cloth, etc.). It depends entirely on the resolution of the capture, of course. I’m not saying meshes can’t be used to model hair or other fine details, they can obviously, but in practice you will never get a decent mesh out of, say, iPhone headshots, while splats will work and capture hair pretty well. There are hair-specific capture methods that are decent, but no general mesh capture methods that’ll do hair and foliage and helicopters and buildings.

    BTW I believe there is software that can turn point clouds into textured meshes reliably; multiple techniques even, depending on what your goals are.