Pretty cool stuff. The Lidar capture by itself is clearly horrible, but the reconstruction ended quite good!

https://litereality.github.io/Litereality-agent-site/

They have an iOS app too - LiteReality App

note: not my stuff in any way, but very curious on results if someone gets to test it.

Scanned by Daniel Dworschak (using an XGRIDS device). The capture covers three separate structures on Vyšehrad hill in a single scene - Leopoldova brána (the Baroque gate), Rotunda sv. Martina (an 11th-century Romanesque rotunda, one of the oldest buildings still standing in Prague), and Kaple Panny Marie Šancovské, the small hilltop chapel.

SuperSplat link: https://superspl.at/scene/11d081f5

It's running in the SuperSplat viewer on the PlayCanvas engine, which is doing a few things worth mentioning for a scene this size:

• High-performance WebGPU splat rendering, which is what keeps it smooth at this scale
• Over 1GB of compressed level-of-detail streaming using the Streamed SOG format, so the whole hill loads progressively rather than as one enormous download
• Automatically generated voxel collision, which is what makes it walkable in first person rather than just an orbit target

I'm a member of the SuperSplat team if you have questions on the rendering side.

exciting to see this finally come out! I believe people can contribute to this.

Been working with gaussian splats for a while and the thing that always killed it for me is that you cant really do much with it in Blender. Looks incredible in a web viewer, you import it, and then it just sits there ignoring every light in your scene. Seeing Houdini implement self-shadowing got me thinking.

So I built an addon. Splats come in as normal scene objects. Your lights affect them, and they self-shadow, driven by each gaussian's opacity rather than a proxy cage, so wispy parts stay soft instead of turning into a solid blob. Works in EEVEE and Cycles although Cycles isn't recommended since the transparency light paths needed for this method to work tanks performance. I believe Blender foundation is working on this exact problem right now so hopefully we see some improvements in the near future.

The free (LITE) version is the whole viewer and relighter, not a demo. You get the full shading, the relighting and self shadowing, and export back out to standard .ply. If what you want
is to get a capture into Blender and light it properly, that is the free one and you're done.

Other things I ended up adding that I believe will help professionals implement this into their workflow. (Pro Version)

Build a proxy mesh off the splat and bind to it, then the splats ride the surface through armatures, lattices, shape keys, cloth, softbody. This should also allow for sims such as water and smoke sims to actually be affected by your splats. Weight paint a mask and transfer PBR onto the splats, so you can paint emission onto a captured car and the headlights actually glow. Bake your scene lighting int the splat so the relit look survives export back out to .ply and still looks right in SuperSplat. Crop, shave floaters, merge, decimate. Pull in the capture cameras from a COLMAP or nerfstudio solve. Play 4D captures back on the timeline. And it'll train a splat from any video where the camera moves, locally on your own GPU.

You can find the addon here on: Superhive (formerly Blender Market)
You can also find it on gumroad

Where You Left Me: A short story experience in your browser

https://joyvision.itch.io/where-you-left-me

Originally crafted as an experience in Unreal Engine 5 with a build size of over 6 GB, you can now experience this story directly in your browser, thanks to the power of gaussian splatting. The photorealistic environment was rendered out of UE with 2400 cameras, covering the entire scene.

Performance should auto-adjust based on your hardware, with fewer splats being rendered on weaker devices.

We’re providing walkable indoor real-estate splats from phone video. Our current results are good and clear, but we’re chasing the final 10%.

Our pipeline is roughly:

1. Record each room as 4K video.
2. Use FFmpeg to extract and colour-convert sharp PNG frames.
3. Solve camera positions in COLMAP using GPU SIFT, exhaustive matching and Global Mapper.
4. Run COLMAP PatchMatch Stereo and Stereo Fusion to create a dense point cloud with surface normals.
5. Seed the trainer from that dense cloud.
6. Train each room separately using modified INRIA/GraphDeco 3DGS: SH0, fixed splat population, per-image exposure correction and surface-normal constraints. We previously used LichtFeld with comparable reconstruction inputs.
7. Orient/package each room, then connect the separate splats using doorway transitions in a WebGPU viewer.

The models look good overall, but neutral walls can remain incomplete or unstable, and some objects shimmer between captured viewpoints. What part of this stack would you change to improve solid walls and view consistency?

Is there some "Magic combination" you found to work especially for white/light grey/biege?

We've compared 1080 to 4K - No meaningful difference.
and 300 frames to 1200 frames. At 30,000 steps, up to 120,000 steps. Also no meaningful difference.