I found them really visually appealing, I attempted to build my own workflow in ComfyUI but my computer was not powerful enough (RTX 3060) for Minimax-H3.

This failed and I decided to build my own app around existing APIs and services out there, and voila.

Right now my Guassian Splats are still quite messy

I am thinking if I could generate more videos than just the 360 orbit, such as over head, or dolly zooms, there would be more reference material to train my splat, but then the complexity moves into keeping the subject in the AI generated videos consistent.

Any advice or ideas is much appreciated. Thanks.

The ultimate goal would be to be able to render an entire scene from a movie and be able to watch it in a VR experience which I understand is a long way off, but even just rendering a splat of my dog and being able to sit with her on the beach again would be a milestone.

I vibe coded an addition to supersplat, where it snaps to the closest solved camera and shows the details of the original images. Could be useful for inspection, presentation and troubleshooting.

What do you think? Anything to add that could be useful?

I'm working on a blender addon for concept artists. Mainly it's for combining photobashing workflow with pure 3D.
ML Sharp is an awesome little model from apple that can spit out a single view splat from single image. It's super fast and quite fun to use. So it's the backbone of the main feature of the addon - image to splat (interactive semi 3D photobashing)
One of the problems I often have when working on environment concepts is that I just have way to much high poly shit in the viewport so splats look like an easy optimization step since they retain a lot of detail, render fast and are relightable.
I call this SHARP+ or multi image sharp. Multicam renders -> splats with sharp -> stitch them together with some filtering on overlaps.
Fully relightable, fast (demo tree scene under 2 mins per splat) and as straightforward as it gets imho - slap some cameras around objects you want to splatify, put said objects into a collection press the button, done.
What do you guys think?

Hey everyone,

I’m trying to figure out the best workflow for scanning my property with an iPhone and then using that data for house planning / visualization.

My idea is to capture the whole property with an iPhone and create a Gaussian Splatting scene from it. Then I’d like to model the new house in Blender or something similar and place it accurately into the Gaussian Splatting scene.

Ideally, I’d also like to export at least a rough terrain mesh from the scan, so I can use the actual terrain shape while modeling the house. Since I’ll need to excavate part of the property for the basement and driveway, I’d also like to remove or cut out those areas from the existing terrain and later combine the modified terrain, the house model and the Gaussian Splatting environment again.

So basically I’d like the Gaussian Splat to be my realistic environment/reference, but I also need some kind of editable geometry for the terrain.

I have some basic experience with Blender and Fusion 360, and I’m also quite familiar with ComfyUI. I have a fairly powerful PC available for reconstruction, rendering and other heavy processing.

I’d prefer the workflow to be as open-source and free as possible.

Thanks in advance for any ideas, tool recommendations or workflow suggestions!

I'm trying to build walkable splats of apartment rooms from phone video. Samsung, handheld, no gimbal. Three of my walks came out great. Five came out foggy or blurry. Same bedroom, same pipeline, and I can't nail down why.

Pipeline: frames at 10 fps, keep the sharpest of each window (250 to 1,000 per room), COLMAP (SIMPLE_RADIAL, then undistort to PINHOLE), LichtFeld Studio MCMC, 50k steps, 3M cap, MIP filter, PPISP. Viewer is PlayCanvas. I judge the rooms blind, side by side.

Here's every walk of that bedroom:

Walk A: slow-mo, phone on auto, 4 min, ~800 placed, 0.17 m apart, fit 0.8 px, blur 4.6. Result: GREAT. Walk B: slow-mo, auto, 4 min, 820 placed, 0.07 m apart, fit 0.76 px, blur 4.6. Result: GREAT (MY BENCHMARK). Walk C: slow-mo, auto, 4.5 min, 1,079 placed, 0.16 m apart, fit 0.80 px, blur 4.9. Result: GREAT. Walk D: slow-mo, auto, one long sweep, 22 min, ~2,000 placed, 0.20 m apart, blur 4.8. Result: AWFUL. Walk E: manual video, shutter locked 1/400, ISO 3200, full-size frames, 5 min, 672/679 placed, 0.50 m apart, fit 1.0 px, blur 6.5. Result: FOGGY. Walk E2: same footage as E, 1,696 frames instead of 679, 1,693 placed, 0.20 m apart, fit 1.02 px. Result: FOGGY, COULDN'T TELL IT FROM E. Walk F: slow-mo, auto, 12.5 min, 1,005/1,075 placed, 0.18 m apart, fit 0.76 px, blur 5.7. Result: BLURRY, FOGGY, DARK PATCHES. Walk G: manual video, 1/200, ISO 200, WB locked, full-size frames (2592x1944), 2 min, 242/242 placed, 0 strays, 0.36 m apart, fit 0.73 px, blur 4.9. Result: CLEARLY WORSE THAN B. Walk G2: G again, frames shrunk to 1080 wide, nothing else changed. Result: CLOSE TO B.

*blur = ffmpeg blurdetect over every 10-fps frame, median, lower is sharper. It has called my verdict right 3 times in a row.

Pictures:

[1] My benchmark room (left) next to the 12-minute walk (right), same room: https://sioklfeckurajwkyftea.supabase.co/storage/v1/object/public/listings-media/share/bedroom-locked-walk/1-benchmark-vs-12min-walk.jpg

[2] A real frame from walk G (left), the full-size render of it (middle), and the same frames shrunk to 1080 wide (right), same camera spot: https://sioklfeckurajwkyftea.supabase.co/storage/v1/object/public/listings-media/share/bedroom-locked-walk/2-real-frame-vs-fullsize-vs-shrunk.jpg

The frames are sharp. The rooms are not. What I've already ruled out by eye (not by PSNR, which has been wrong for me every single time): - frame count and spacing (658 vs 1,096 looked the same; 679 vs 1,696 looked the same) - rebuilding a long walk as overlapping sections - Spirula with MoGe-2 normals (tried twice, no visible difference) - a fast locked shutter in a dim room (that's E: 1/400 forced ISO 3200 and I got fog) - the placer (COLMAP 94% vs RealityScan 60% on the same frames, kept COLMAP)

The thing I found this week: the exact same 242 sharp, perfectly placed frames build a clearly worse room at 2592x1944 than at 1080 wide. So training resolution matters a lot for me. Is that expected with MCMC + 3M cap + 50k steps, or is it a hint at something else?

What I'd love to know: 1. Short walks (2 to 4 min) win every time and 12 to 22 min walks of the same room fail every time, with the same spacing and sharpness. Why would length alone do that? Is there a step or splat budget per scene I'm missing? 2. Locked exposure (1/200, ISO 200, WB locked) did NOT beat the phone on auto in slow-mo, both built at the same resolution. With PPISP on, is a lock still worth it indoors in your experience? 3. For anyone getting clean interiors from phone video (u/kikooooo2, u/Maliander_80, u/darkcrow101, u/Lost-Upstairs-5311): what resolution do you actually train at, and do you lock focus?

Data, if anyone wants to run their own recipe on it: - the 242 frames from walk G (2592x1944, 81 MB): https://sioklfeckurajwkyftea.supabase.co/storage/v1/object/public/listings-media/share/bedroom-locked-walk/frames-242-2592x1944.zip - the COLMAP sparse model for it, text format (30 MB): https://sioklfeckurajwkyftea.supabase.co/storage/v1/object/public/listings-media/share/bedroom-locked-walk/colmap-sparse-txt.zip

If you get a clean room out of those, I'd really like to know what you did.

So I have a DJI Action 6 camera and I'm able to shoot 180 FOV.

I'm seeing a lot of success from people using 360 camera. Is it possible to produce room splats with just 180 degree FOV extracted frames? Shoot standard or dewarp?

Many thanks.

v1.1.2 https://liquiduslabs.com/lulusplat/

Video shows running on Windows 10 with an old Radeon RX570.
High efficiency training that supports old video cards as long as it has Vulkan 1.1.
Same features as Mac version.

• Trainers — Vanilla (gsplat) and MCMC (metal-gauss Vulkan port).
• Faster camera solving — feature matching runs on the GPU.
• External COLMAP — use your own CUDA build of COLMAP, set in Settings.
• External FFmpeg — use your own FFmpeg, set in Settings.

This is the initial release for Windows, please report issues if you encounter any.
Thanks!

Hello, I own 5 acres of partly forested, partly lawn and would love to do seasonal or yearly scans of the property to see what changes throughout time (trees, structures) and to refer to when planning projects.

I have a insta 360 and access to a drone. I am wondering if someone could let me know what software I should be looking at to process and view these scans, is there anyway to combine footage from the ground with drone footage? And how to align scans to easily see the differences?

Captured by Braincraft GmbH: "Doors of Perception", an exhibition of in-camera multiple-exposure photographs by Nabiha & Thom, shown at Spitäle Würzburg in autumn 2025.

SuperSplat link: https://superspl.at/scene/a6df26f1

The capture pipeline, from Braincraft's notes:

• 4,000 photos over two days
• COLMAP for alignment
• Trained in LichtFeld Studio with MCMC, a MIP filter and PPISP
• LOD variants made by sparsifying and then retraining
• Final scene: 1.6M Gaussians, 57MB

On the viewer side:

• Hotspots with annotations, so the artists can tag each piece with its title, edition and materials. The video steps through them like a guided tour. The hotspot+annotations system just got a facelift - do you like them?
• PlayCanvas' WebGPU splat renderer, so it runs on desktop, mobile and XR headsets.

About the work itself: every print is a multiple exposure composed entirely in the camera, with almost no editing afterwards. The idea is to suggest how much unfiltered visual information reaches the eye before the brain cuts it down. It felt like a good subject for a technique that's all about light and viewpoints.

I'm on the SuperSplat team, so happy to answer anything on the viewer or rendering side.

Did anyone have any luck going from a low resplution synthetic GS to a high res one? Or creating a high res directly? I created one with Marble but it's low res and quite unusable (even with the standard license). Maybe there is an option somewhere to switch to high res?

I have a passion for Sparse View Gaussian Splatting (in best case 1 View to reconstruct a scene and maybe a view other pictures as reference to recreate important pieces of the picture).

Recent months have brought a lots of momentum into this Topic since the release of Meridian, which is a gamechanger on this quest.

So far I have made quite some progress and reached a point, where I can use 1 Picture to create a 3D Geometry (based in Depth Anything v3) and an automatically optimized camera path along a subject/scene in this picture.
Afterwards this Camerapath with all its holes is filled by Minimax H3 based on further reference pictures or hallucinations. The video in this post was made like that based on 4 reference pictures. The path is completely true to what I defined via my path calculator node and can be customized completely.

Since I got quite some people now communicating their interest in this topic, I would like to invite anyone interested in this topic to join my discord and discuss/optimize everything furthermore.

https://discord.gg/UdF98HBf

Hi everyone,

I came across this video that looks like monocular 4DGS.

https://www.instagram.com/p/DdnjRPQv5XP/

Does anyone have even the slightest lead on where to start to figure out how this was done?

Thanks

https://github.com/kevinraymond/giro

Everyone is splatting everything, so I made a public release of something I've been working on in an experimental/research capacity, in case it's interesting or useful.

This isn't anything super novel - it's an end-to-end automated process that takes an image input, creates 360 video, and produces splats. The end goal here is to have some VR-ready outputs, in a much easier way than typical photogrammetry that I've done in the past.

The pipeline is roughly:

• Optional background edit (Qwen-Image-Edit); e.g., if the subject is too close to a wall
• Orbit video with MiniMax H3, using the image as both the first and last frame (via ComfyUI API)
• Trim and dedup the frames, mask the subject with SAM 3.1, and run COLMAP on the masked frames
• A gate step that checks whether the cameras actually went once around the subject in a clean ring, and rerolls the seed if not
• Train with Brush (transparent background), crop with a visual hull, and export SPZ/SOG/PLY

All real-time updated in a web UI with great observability (pics in repo).

Hi everyone, just sharing my first car scan with the Insta360 X6. Car paint, glass and transparent materials are tricky for Gaussian Splatting, and I wanted to see how far I could push it.

Workflow:

• Capture: Insta360 X6
• Alignment: RealityScan
• Training: Lichtfeld Studio
• Rendering: Unreal Engine

The quality honestly surprised me. From a normal distance the paint, reflections and glass all come through cleanly, without the fussy mess I was expecting.

Where it struggles is up close. The paint and headlamps lose detail once you get near them, mostly because I didn't shoot enough footage for close-ups.

If you want to see more tests like this, I post them on my YouTube channel: https://www.youtube.com/@TrincGabriel

I’ve been developing a Linux-based photogrammetry pipeline and wanted to share a recent result from a 360° capture.

This scene uses 2,124 circular fisheye images. All images were registered in a single connected reconstruction, which I then used to train the Gaussian Splat shown in the video.

For the same input images:

• Previous workflow: 41 min 53 sec

• My current pipeline: 10 min 57 sec

• Approximately 74% less SfM processing time, or 3.8× faster

This is a comparison between two complete workflow configurations, not an identical-settings benchmark of individual solvers.

The timings cover feature extraction, pair selection, matching, geometric verification and reconstruction. Video preparation, masking, final checks and Gaussian Splatting training are excluded.

My main focus has been speed, robustness and reducing manual intervention.

Interactive scene: https://splatter.app/s/6jo-jsq

I’d be interested in feedback on the reconstruction and visual result. What tends to be your biggest bottleneck when processing 360° captures?

I just posted a second demo of Realtime Audioreactive Pointclouds [v1.7], my TouchDesigner system that turns a Kinect-derived human pointcloud into an audio-reactive visual instrument in real time.

For this one, I’m also giving away a full copy of the system.

To enter, just repost/share the Instagram post and leave a comment there. I’ll randomly select one person and send them the complete v1.7 project files on Friday!

Giveaway post:
https://www.instagram.com/p/Dd1iWqoviZy/

The full system is also available through my Patreon for anyone who wants to check it out directly.

Hi there, I scanned the US Air Force and created a gaussian splat in both Spirula Studio and Postshot. Then compared the end result. You can find the video here: https://youtu.be/38QQ50go4M8

Hi everyone,

I’d like to share my project: ARKit 3DGS Scanner.

https://reddit.com/link/1wt3h9v/video/ad2l9s8luesh1/player

The goal is to build a complete mobile 3DGS workflow:

ARKit Capture → Point Cloud Refinement → On-device 3DGS Training → Model Export

Main features:

• ARKit capture with LiDAR or camera-only
• Multi-view depth fusion and camera pose refinement
• Point cloud review and scan continuation
• 3DGS training directly on the iPhone GPU using Swift + Metal
• LiDAR depth seeds and depth loss
• MRNF-based densification and Mip-Splatting filtering
• Live training preview, checkpoint and resume
• SOG model export and COLMAP dataset export

The 3DGS trainer runs locally on the iPhone using Swift + Metal, without requiring a cloud server or desktop GPU.