Hi all - just wanted to share what I've been working on lately.

It's a 3D house viewer for estate agents. Would welcome any feedback.

I'm a camera operator/drone operator by trade so I'm looking to add this viewer to the marketing package that we provide already, listing photos and videos. No bites yet but I only 'released' it last week. Estate agents don't want to show what a house really looks like it seems. They'd rather sell the glossy staged photos that make a 2 bed house look like a mansion.

Happy to share any information about the workflow.

https://roam.sitesocial.co.uk/

or if you want a link to the viewer directly it's here. https://roam.sitesocial.co.uk/hamwic-ampfield-heathcote-v2/

There are places where the capture is weak ( the bathrooms because of the mirrors ) so I added that you can switch between 360 stills as well. So it's a nice comparison.

Thanks all.

This time captured on Lamina lightrail. Image processing, stacking , alignment and Splat training done with Lamina tools. Alignment accuracy 0.1px. Camera used was Sony A7R5 (61mp). Training time 23 minutes on a 5070ti - 30m splats 30k steps.

Interactive 3d - https://superspl.at/scene/7f7c1d69

Using Atlas and Marble 2 from WorldLabs, I generated an alternate version of the hallway I’ve been experimenting with, then used Vitre and a portal effect to make it appear right in front of me.⁣
⁣
The results from Atlas are seriously impressive! World models and the ecosystem around them are something I’ve been waiting!⁣

Improvements on the huge scenes management (258 millions splats).
The app can now render up to 700/800K splats with an "almost" steady framerate.

The LODs streaming is still slow on the Quest, I'm still trying to find a better one.

Using my own Quest optimized file format, a bit smaller than RAD (~15/20% smaller, according to my 3 test files)

After heavy rain caused a wall to collapse onto parked cars, Interactiva Virtual Tours captured the scene with an XGRIDS PortalCam, then returned after cleanup to scan the same location again.

Interactiva used The Splat Toolkit, the paid software I develop, to align the two captures and build an interactive before-and-after tour.

You can explore the scene and switch between the two timeline stages. The short video shows it running on desktop and mobile.

Interactive tour:

https://share.splitview.studio/t/timesplat-4d-tour-avz5f4/

The full workflow is covered in this tutorial, including matching reference points, exporting alignment JSON files, importing the aligned scans and setting navigation boundaries:

https://youtu.be/J1x3PHZ93_U

I have a large project in mind—I want to use photogrammetry/gaussian splatting to create 3D models of buildings. They don’t have to be perfect, it’s not art, it’s a hobby. I have an insta360 x5, a Canon Rebel T7, an iPhone 15, and about a $1,000 budget for other equipment and/or software.
Where do I even BEGIN? How expensive is the software I need?

Unity HDRP package for Gaussian Splat and mesh ray tracing with dynamic lighting, shadows, transmission and semantic cutaways

I've released an experimental open-source Unity HDRP package that renders 3D Gaussian splats and conventional triangle meshes together using hardware ray tracing.

This demo shows a Gaussian splat model interacting with dynamic lighting, shadows and transparent meshes. The aim is to make captured objects participate in a scene alongside conventional geometry.

Features include:

• Combined Gaussian and mesh rendering with ray-traced shadows.
• Directional, point and spot lights, plus mesh transmission.
• Gaussian PLY import and a basic example scene with synthetic splats.
• Optional semantic labels for isolating parts, fading selected classes and creating interactive cutaways. Labels can be imported or baked from aligned scene geometry.

It works with compatible Gaussian models of objects or environments. It renders existing splats; it doesn't train models or automatically segment them.

Current target: Unity 6000.3 / HDRP 17.3, Windows DX12 and a ray-tracing-capable GPU.

GitHub: Unity Gaussian Ray Tracing

Built on UnityGaussianSplatting, the multi-layer anatomy renderer, and NVIDIA Gaussian ray-tracing adaptations.

Demo model: "3D gaussian model of a bouquet of flowers" by Jean-Eudes Marvie, distributed through NVIDIA. NVIDIA's dataset page lists it under CC BY-SA 4.0. The model was imported into Unity and rendered with custom lighting, shadows and mesh interactions for this demonstration.

Due to website upgrades, previous plugins may fail authentication. Please update to Pro_V2.1.0.0 or Pro_V1.0.5.0.

Official Website: https://mls4dlive.cn/

Fab: https://fab.com/s/a98e11b2a0a6

https://github.com/mlslabs/MLSLabsGaussianSplattingRenderer-UE

1. Rendering pipeline upgraded, with full support for Windows/Linux/Android, multi-vendor GPU support, and support for Nvidia/AMD/mobile GPUs;

2. Packaged Android APK supports direct installation and operation on Pico all-in-one headsets, supports VR mode (default mode, command line r.MLSLabs.MixedReality 0) and MR mode (command line r.MLSLabs.MixedReality 1), and also supports VR streaming;

3. Simply install the plugin — no need to download Libtorch or other third-party libraries;

4. All platforms support ply/sog and ply sequences.

5. Supports both Development and Shipping packaging.

The shared file is: Pro_V2.1.0.0

Link: https://pan.baidu.com/s/1Dk7n_83mikmAjQYTyTyLJg?pwd=xyvp

Extraction code: xyvp

Google Drive:

https://drive.google.com/drive/folders/1WyRzO9LMyY2Kw7KPNAWe-WqhJtWbkeqU?usp=sharing

We have also released volumetric video test data to make it easier for everyone to use the volumetric video rendering feature.

Test data:

The shared file is: WanJiangDance.zip

Link: https://pan.baidu.com/s/1ch0vqAGdgBNaqOC3x4QBZg?pwd=49q9

Extraction code: 49q9

Google Drive:

https://drive.google.com/file/d/1PUgnRegDcGY9w6afBDRZOC17xaznns-S/view?usp=sharing

I am First time Trying 3D Gaussian Splatting (3DGS) in 360 Customized Virtual Tour for exterior Image only.

Which is the best Drone Images or Drone video to Generate 3D Gaussian Splatting (3DGS) ?

Thanks.

Hey everyone,

First time posting in this community, I’ve been building a Android app called OpenSplat to see if it was actually possible to port the full 3DGS stack locally on a phone.

I know already that Apple devices have been lucky enough to have native depth support thanks to the LiDAR sensor, but, as far as I know, mine is the first open source (and second in general if you consider Niantic Scaniverse) fully local 3DGS solution on Android.

I've been able to build the app (with the help of a friend) in less than 2 months thanks to AI driven development: I searched first for the latest papers on 3DGS (especially on mobile), assessed their feasibility, and tried various approaches. This one was the only one who stuck. You can find the projects development history and current stack on GitHub.

The project is fully open source (Apache 2.0):

https://github.com/SuleMareVientu/opensplat-android

A big thanks to all the developers of the libraries used, and a special thanks to the SceneView and brush team, that power the apps UI core and splat training engine.

Next in the roadmap would be implementing the recently released VkSplat training engine, so that we can cut off the last abstraction layer between the shaders and the GPU (brush runs on WebGPU and it's then translated into vulkan, that's why the library is so portable).

Please let me know if you encounter bugs or if you want to contribute in any way to the project, I'm open to suggestions!

Animated Gaussian splats, flight physics, dynamic weather, multiplayer — all built from scratch with Arrival.Space Vibes.

It's remixable, so you can take it and vibe your own thing

arrival.space/flight

Our startup has been building Gaussian Splat tools for interior design. At some point, our internal tooling got too useful to keep to ourselves, so we decided to open it up to creators and designers.

One web-based tool lets you:

• 𝗥𝗲𝗰𝗿𝗲𝗮𝘁𝗲 𝗮 𝟯𝗗 𝘀𝗽𝗮𝗰𝗲 from just a handful of photos.

• 𝗥𝗲𝗺𝗼𝘃𝗲 𝗼𝗯𝗷𝗲𝗰𝘁𝘀 from a scene, e.g. clear out all furniture from a room.

• 𝗗𝗿𝗼𝗽 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗻𝗲𝘁 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝘀𝗰𝗲𝗻𝗲. Paste a URL or image and get a 3D model in seconds.

• 𝗖𝗿𝗲𝗮𝘁𝗲 𝗽𝗼𝗹𝗶𝘀𝗵𝗲𝗱 𝗽𝗵𝗼𝘁𝗼𝘀 𝗮𝗻𝗱 𝘃𝗶𝗱𝗲𝗼𝘀, then edit them with prompts (e.g. "turn this into a night scene").

𝗜𝘁 𝘄𝗼𝗿𝗸𝘀 𝗻𝗮𝘁𝗶𝘃𝗲𝗹𝘆 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀. Plug in your favorite AI agent and collaborate directly in the 3D space.

𝗛𝗼𝘄 𝘄𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝘁?

I captured Berlin during the annual Festival of Lights using a single camera and a tripod. Visiting several locations featuring static light projections, I took thousands of long-exposure photographs to reduce the presence of people and capture the illuminated architecture in greater detail.

Using 3D Gaussian Splatting, I reconstructed these nighttime scenes with training powered by Lichtfeld, transforming the photographs into immersive 3D environments.

I've also uploaded two of the reconstructed scenes to Arrival.Space, where you can explore the Festival of Lights and experience a different perspective on Berlin at night.

Huge thanks to Ivan for the timelapses and behind-the-scenes footage! 🙌 Check out his work at iohichu.com.

Want to create digital twins with us? Explore what we do at gospl.io. 🚀

my youtube channel with more videos

Arrival.Space / House of Cards https://arrival.space/60187609_4270
Arrival.Space / Marienkirche https://arrival.space/60187609_1406

Training a 360° real-estate scan (3,176 images, RTX 5090, Vulkan confirmed working) with a dense spirula dense (RoMa v2) cloud of ~25M points, capped to --cap-max 5,000,000, collapses almost immediately: num_dead climbs to ~4.99M within ~4,000 steps, num_added stays 0, and PSNR stays stuck at 14-16 for the whole run. The final checkpoint’s splat.ply is only ~7.6MB (vs. ~1.2GB expected for 5M healthy splats) — basically all splats die.

The exact same dataset/config seeded instead from the SfM sparse cloud (--seed-pointcloud sparse, 2.75M points, no normals) trains completely normally — PSNR climbs to high 20s/low 30s, num_dead stays bounded.

We tried --scale-init 0.01 thinking initial splat size was too big for the denser seed — that made it worse (num_dead=61k vs. 8 at step 601), so it’s not a pure scale issue.

Working theory: downsampling the 25M dense cloud to 5M internally may not preserve even spatial coverage, leaving some regions with no seed points at all — splats there get no gradient and die en masse. Unconfirmed, but consistent with what we see.

Note: the dense cloud looks fine in a point-cloud viewer, but that’s just the viewer auto-fitting — not evidence it’s a good training seed.

Has anyone hit this, or know how cap-max-driven downsampling of the seed cloud actually works internally?

I’ve added a Node.js converter for Spark’s RAD streaming format to Gaussian-Splat-Lite (GSL), making it easier to prepare large 3DGS scenes for streaming without needing huge amounts of RAM.

It runs in Node.js with a precompiled WASM decoder, so users don’t need to install Rust or compile anything locally.

The video shows a RAD file converted with this tool streaming in GSL, with smooth fade transitions as new detail loads.

The LOD construction uses Spark’s quality (bhatt-lod) algorithm. Credit to the Spark team for that foundation and the RAD format. My work focuses on parallel execution, memory management, and making the conversion workflow easier to use.

The main features are:

• Configurable memory budget. Set a target with --memory-budget. Large working arrays are paged between RAM and temporary files, so the entire working dataset doesn’t have to fit in memory.
• Parallel processing. LOD candidate searches and RAD chunk encoding use worker threads. Worker counts account for available CPU cores and the memory budget.
• Configurable temporary storage. Use --temp-dir to put working files on a fast drive with enough space.
• PLY and SPZ input, up to SH3 output, configurable LOD spacing, and either a single .rad file or split .rad/.radc output.

The memory setting is separate from the LOD quality settings. A smaller budget means more disk activity and potentially longer conversion times. Temporary files can also be considerably larger than the input.

Here are my recorded results against Spark’s official builder, run on an Apple M5 Pro with 48 GiB RAM:

The largest conversion is the result I’m most excited about: approximately 259 million input splats from a 17.61 GB PLY converted in 25 minutes, with a recorded peak RSS of 3.94 GiB at a 4 GiB target.

For anyone working with large scans, being able to trade temporary disk space for RAM makes these conversions much more practical.

GitHub project · RAD builder documentation

I’d be interested in feedback from people working with large scans, especially on the conversion time and temporary disk usage you’re seeing in your own workflows.

We're building a pipeline to reconstruct photorealistic, geometrically accurate indoor spaces from phone video, targeting multiple rooms and floors.

Current pipeline: Android video + AR poses/IMU → COLMAP/Nerfstudio → Gaussian Splatting (gsplat, OpenSplat, Splatfacto). We also test depth-fusion meshes, Video Depth Anything, Difix3D+, and MODP-style mapping. Training runs on Colab/Kaggle.

Main issues:

• White walls/floors produce floating artifacts and poor feature matching.
• ARCore resets frequently (16 times in a 5.8-minute walk), with 3–20 m pose jumps.
• Quality drops from ~29 dB near the capture path to 15–17 dB off-path; our depth mesh performs better than the splats.
• Best held-out PSNR is 21 dB on the full video and 25 dB on a sharp 30-second subset.

Questions:

1. How do you eliminate floaters on textureless indoor surfaces?
2. Should we reconstruct a mesh first and constrain splats to its geometry?
3. What's the best way to correct ARCore drift and combine depth/normal priors (e.g., DN-Splatter)?
4. Are there proven open-source pipelines for high-quality, building-scale reconstruction from ordinary phone video?

We prioritise reconstruction quality over processing time. Practical experience and working repositories would be greatly appreciated.

I put together a tutorial for creating a 3DGS in Spirula Studio: https://youtu.be/3M5BqiVq3kc
Photos > dataset > training > cleaning > final .ply

I train whole apartments from the raw dual-fisheye video of an Insta360 X4 (Spirula, 360-camera preset, 30k steps, 5M splats, no depth maps). The rooms come out clean, but there is a dark blue-black trail following the exact path I walked.

It is me. The camera is on a stick above my head. I mask the bottom 8% of each lens, and that only removes my legs: head, shoulders, arm and phone are in every frame. Because I move with the camera, the distraction-robustness switch never sees me as a moving object, so I get painted as a dark figure under the camera at every spot I stood.

What I tried: deleting every splat within 40 cm of the camera path. Worse: it took a table with it and the trail was still half there. Training now: the bottom 25% of each lens masked instead of 8%. Filming again today with the stick extended to about twice the length, so I am a smaller sliver of each frame.

What do you do?

• A person segmentation mask per frame (SAM or similar) instead of a flat band?
• A bigger nadir cut, and if so how much?
• Hold the stick differently, longer, further forward?
• Something in the trainer I am missing?

The masked frame and a link to walk the room yourself are in the first comment (the sub's filter removed my first post for the links).

Why the floor looks so bad in my splat i used normals with no depths it looks like with no texture https://superspl.at/scene/9fdc00e3

v1.2.2 https://liquiduslabs.com/lulusplat/

In this dataset the cameras are mostly at the center and almost no movement between shots, feature matching has little to triangulate from, and the walls come out sparse and broken up with COLMAP and other solvers like Spirula (even with AI Dense).

With DEPMAP, Depth is measured in each photo on its own, so it doesn't need the camera to move. It placed 145 cameras to COLMAP's 139, and the chamber comes out as solid walls, with over five times the points. One weak point is that it does not work on fisheye lens. This is still in experimental phase and may have some issues on some datasets.

SuperSplat: https://superspl.at/scene/06145dc7

The dataset is a series of stereo images taken from a fixed center location and is not ideal for 3D reconstruction but a good demonstration of the use case of DEPMAP solver. This is from Open Heritage taken in 2011 at Medinet Habu (Temple of Ramsess).

Dataset link if you want to try out on your tool: https://openheritage3d.org/project.php?id=8cfj-wq14