255 karma · joined January 18, 2026
There are many ways to relight Gaussian splats. However, the highest quality results are currently coming from raytracing/path tracing render engines (such as Octane and VRay), with 2D diffusion models in second place. Relighting with GSOPs nodes does not yield as high quality, but can be baked into the model and exported elsewhere. This is the only approach that stores the relit information in the original splat scene.
That said, you are correct that in order to relight more accurately, we need material properties encoded in the splats as well. I believe this will come sooner than later with inverse rendering and material decomposition, or technology like Beeble Switchlight (https://beeble.ai). This data can ultimately be predicted from multiple views and trained into the splats.
"Also, environment radiosity doesnt seem to translate to the splats, am I right?"
Splats do not have their own radiosity in that sense, but if you have a virtual environment, its radiosity can be translated to the splats.
Geometric analysis for Gaussian splatting is a bit like comparing apples and oranges. Gaussian splats are not really discrete geometry, and their power lies in overlapping semi-transparent blobs. In other words, their benefit is as a radiance field and not as a surface representation.
However, assuming good camera alignment and real world scale enforced at the capture and alignment steps, the splats should match real world units quite closely (mm to cm accuracy). See: https://www.xgrids.com/intl?page=geomatics.
It's also possible to splat from textured meshes directly, see: https://github.com/electronicarts/mesh2splat. This approach yields high quality, PBR compatible splats, but is not quite as efficient as a traditional training workflow. This approach will likely become mainstream in third party render engines, moving forward.
Why do this? 1. Consistent, streamlined visuals across a massive ecosystem, including content creation tools, the web, and XR headsets. 2. High fidelity, compressed visuals. With SOGs compression, splats are going to become the dominant 3D representation on the web (see https://superspl.at). 3. E-commerce (product visualizations, tours, real-estate, etc.) 4. Virtual production (replace green screens with giant LED walls). 5. View-dependent effects without (traditional) shaders or lighting
It's not just about the aesthetic, it's also about interoperability, ease of use, and the entire ecosystem.
Second, it's very motivating to read this! My background is in video game development (only recently transitioning to VFX). My dream is to make a Gaussian splatting content creation and game development platform with social elements. One of the most exciting aspects of Gaussian splatting is that it democratizes high quality content acquisition. Let's make casual and micro games based on the world around us and share those with our friends and communities.
The most expensive part of Gaussian splatting is depth sorting.
Great job, Chris and crew!
Check this project, for example: https://zju3dv.github.io/freetimegs/
Unfortunately, these formats are currently closed behind cloud processing so adoption is a rather low.
Before Gaussian splatting, textured mesh caches would be used for volumetric video (e.g. Alembic geometry).
You're right that you can intentionally under-construct your scenes. These can create a dream-like effect.
It's also possible to stylize your Gaussian splats to produce NPR effects. Check out David Lisser's amazing work: https://davidlisser.co.uk/Surface-Tension.
Additionally, you can intentionally introduce view-dependent ghosting artifacts. In other words, if you take images from a certain angle that contain an object, and remove that object for other views, it can produce a lenticular/holographic effect.
However, surface-based constraints can prevent thin surfaces (hair/fur) from reconstructing as well as vanilla 3DGS. It might also inhibit certain reflections and transparency from being reconstructed as accurately.
(I'm not the author.)
You can train your own splats using Brush or OpenSplat
And thank you!
But yes, you can easily use iPhones for this now.
So likely RealSense D455.
I recommend asking https://www.linkedin.com/in/benschwartzxr/ for accuracy.
Gaussian splatting is a bit like photogrammetry. That is, you can record video or take photos of an object or environment from many angles and reproduce it in 3D. Gaussians have the capability to "fade" their opacity based on a Gaussian distribution. This allows them to blend together in a seamless fashion.
The splatting process is achieved by using gradient descent from each camera/image pair to optimize these ellipsoids (Gaussians) such that the reproduce the original inputs as closely as possible. Given enough imagery and sufficient camera alignment, performed using Structure from Motion, you can faithfully reproduce the entire space.
Read more here: https://towardsdatascience.com/a-comprehensive-overview-of-g....
I'm David Rhodes, Co-founder of CG Nomads, developer of GSOPs (Gaussian Splatting Operators) for SideFX Houdini. GSOPs was used in combination with OTOY OctaneRender to produce this music video.
If you're interested in the technology and its capabilities, learn more at https://www.cgnomads.com/ or AMA.
Try GSOPs yourself: https://github.com/cgnomads/GSOPs (example content included).
This approach is 100% flexible, and I'm sure at least part of the magic came from the process of play and experimentation in post.
That said, the technology is rapidly advancing and this type of volumetric capture is definitely sticking around.
The quality can also be really good, especially for static environments: https://www.linkedin.com/posts/christoph-schindelar-79515351....
The gist is that Gaussian splats can replicate reality quite effectively with many 3D ellipsoids (stored as a type of point cloud). Houdini is software that excels at manipulating vast numbers of points, and renderers (such as Octane) can now leverage this type of data to integrate with traditional computer graphics primitives, lights, and techniques.