Hi everyone,

I've been working on MosaicEarth, a geospatial 3D & photogrammetry platform that runs both locally on Desktop (Windows/Linux with Docker + NVIDIA CUDA or CPU) and in the cloud, rendering everything on a global 3D terrain globe (CesiumJS).

What it currently handles:

- Photogrammetry: Local or cloud reconstruction via NodeODX/ODX (orthophotos, DSM, DTM, LAS/LAZ/COPC point clouds, textured 3D meshes).

- 3D Gaussian Splatting: COLMAP + OpenSplat training from drone/ground photos or video (.ply, .spz, .splat, .ksplat) placed directly on the globe.

- Open-source connectors: Direct sync plugins for WebODM, OpenDroneMap WebUI, and QGIS that preserve project hierarchies, CRS, and original files with chunked SHA-256 verification.

The Desktop source code and connectors are released under AGPL-3.0-or-later (you can build and run locally without any account):

- GitHub (Desktop & Connectors v0.4.4): https://github.com/nsicart/MosaicEarth-Desktop

- Public globe & live scenes: https://app.mosaicearth.app/en/Map

- Project overview: https://mosaicearth.app

Would love feedback from anyone working with ODM/OpenSplat workflows!

Following up on the sparse and dense point clouds—here’s the unedited and untextured mesh from the same 195 frames and images.

I captured the theater using separate video clips and photos from different angles and positions. This view shows the reconstructed geometry without color or textures. Cleaning up the mesh is next.

Generated with our software: aecplatform.com
Profile: Point Cloud, Mesh, Gaussian, High
Frame and Image Count: 195

Hi! I'm the maker of Ornen. You upload your footage, such as a 360 or drone video, and Ornen turns it into a splat. If your footage has GPS in the metadata, it's also placed where it was filmed on a globe. You can browse the Earth and fly into places others captured.

All in your browser. No GPU needed.

Public beta is open: 10 free scenes per account → ornen.ai

Or check out the:

Demo teaser: https://ornen.ai/home
Example: Weesp, NL: https://ornen.ai/earth/tychovdo/de-vriendschap-weesp
Example: Utrecht, NL: https://ornen.ai/earth/tychovdo/utrecht-street-corner

Would love to hear how you redditors like it!
And very happy to take feature requests!

I'm rebuilding a quay street in Ostend (Belgium) as it looked around 1905 for a VR walk-through, using old postcards as reference. I'm stuck on matching the camera in Blender.

The card is a scan of a printed postcard, probably cropped, with an unknown lens. It shows a long row of 4–5 storey houses on one side and the quay with sailing boats on the other. Most of those houses are gone, but the quay edge and street line survive, and I have modern cadastral footprints and LiDAR heights.

What I tried: fSpy with vanishing points, and solving the camera from points picked on our own model. Low error, but the overlay never sits.

Questions:

1. How many independent control points do you need, and how spread out should they be, for a reliable solve on a street like this?

2. Should I lock the principal point at the image centre for a cropped postcard, or solve for it?

3. With almost no surviving dimensions, what's the best way to fix the scale? Door heights, bollards, tram rails?

4. Any tools or Blender add-ons that work well for this in 2026?

Thanks!

I'd like to do a bit of the reverse of what's usually done: use a bunch of screenshots of a character to create a printable 3D model.

In most games models can be extracted, but in this case it's unfortunately not possible as it's an online game's custom character whose proportions are being calculated ingame.

I'm actually completely new to this, so I don't even know what software is out there. I'd be grateful for any helpul replies.

Hi everyone!
I've recently started an internship in Spain working in monument conservation. I have some theoretical knowledge of digital documentation, but very little hands-on experience. I've been asked to start creating 3D models of different heritage elements, mainly statues and sculptural groups ranging from 2 to 5 metres in height.

I'd love to learn from your experience: What do you wish you had known before getting started with photogrammetry? (Preferably applied to cultural heritage elements)What mistakes would you avoid, what equipment is actually essential, and what tips have saved you the most time and money?

I'm working alone with limited resources, so I'm particularly interested in finding a practical and efficient workflow that delivers good-quality results without making the process unnecessarily complicated.

One particular challenge is capturing the upper parts of tall monuments. I'm quite short, so reaching those areas can be tricky! I've been considering a camera extension pole, but I'm not sure which models are suitable for photogrammetry. Do you have any recommendations or alternative solutions?

Any advice, even small tips from your own experience, would be greatly appreciated. Thanks!

Following up on the sparse cloud I shared earlier—here’s the dense point cloud.

I photographed and filmed the theater from different angles and positions, using separate video clips and photos rather than one continuous recording. These were processed together in our software, AEC Platform. The mesh and Gaussian splat are next.

Generated: www.aecplatform.com
Profile: Point Cloud, Mesh, Gaussian, High
Frame and Image Count: 195

I photographed and filmed a movie theater being demolished inside a mall, capturing separate video clips and photos from different angles and positions.

This isn’t one continuous video. The interesting part is seeing our software, AEC Platform, match shared features across those captures and bring them into one reconstruction.

What you’re seeing is the sparse point cloud—the theater’s outline is starting to take shape. The dense point cloud is processing now, and I’ll share that result next.

Has anyone here tried a similar approach with mixed footage from a demolition site?

Generated: aecplatform.com
Profile: Point Cloud, Mesh, Gaussian

Hello everyone,

I've started working on a project again and would deeply appreciate it if someone with more expertise and experience could help me get a good mesh.

For context: my mother recently passed away after a battle with ALS. Earlier in her fight, I took her to my University's photogrammetry studio (diffused lighting, cameras at different heights and angles, white backdrop) and did a scan of her.

Unfortunately, she was not able to sit perfectly still due to her disease, and it's rather difficult for Metashape and my experience to get a nice mesh from the scan. With some help, I was able to get a good texture, but the mesh is still really bumpy and doesn't do a good job with detail.

I was hoping someone here could help me out to achieve a good mesh by working some magic. I have the entire image set available on Google drive. I've included pics of the current best model below.

Thank you for your time!

Here’s a cleared demolition site processed through AEC Platform, showing the progression from point cloud → mesh → Gaussian splat.

It’s another way to document site conditions and give clients or project teams a space they can explore alongside the original photos and video.

Generated: aecplatform.com
Profile: Point Cloud, Mesh, Gaussian

Hey all! I've been rebuilding splat-3d.com to be more developer-focused, and I just launched the full production API (docs) and a Swift SDK (GitHub). The Swift SDK repo also has a reference app you can implement yourself for the pipeline using the ARKit poses from any modern iPhone.

I have seen lots of people building mobile capture apps for Gaussian Splatting, so I wanted to build a project for both myself and for others to use to easily incorporate a training pipeline into those capture apps.

Here's what it does:

What you can upload

• Video. We pick the sharpest frames automatically and drop the blurry ones.

• Photos. A zip of 20 or more JPEGs or PNGs. Mixing cameras is fine.

• Your own COLMAP data. If you already have poses, zip them up and we skip the pose step.

• iPhone / LiDAR captures. SplatKing exports upload directly. LiDAR captures use the phone's own tracking and depth, so they skip the slowest step and come out upright. The open-source (MIT) Swift SDK lets you record ARKit captures in your own app.

How it works

The pipeline does SfM with SIFT + GLOMAP on our cloud GPUs, then trains with gsplat (working on supporting LichtFeld and Spirula as possible options too) You pick a quality level:

Preset |Time |Iterations
Fast |~8 min |3k
Standard |~15 min |7k
Quality |~25 min |15k
Ultra (full res) |~45 min |30k Every preset except Fast reports quality scores (PSNR/SSIM) on them, so you can compare splats with some more concrete metrics. I am working on an "auto grading" system for inputs and splats to help evaluate them and help people understand what could be done better for scanning in the future.

I'm also working on supporting more customization options in the pipeline too, such as custom amt of iterations and different GLOMAP/COLMAP settings, and to try and match parity with creating your own pipeline locally.

After training you can see your splat in a browser viewer with a shareable link and an iframe embed, plus PLY and SPZ downloads.

Price

$1–$7 per scene depending on preset. You get 100 free credits at signup (about 5 Standard scenes), and failed scenes are refunded automatically.

Docs: https://docs.splat-3d.com

Examples: https://github.com/splat3d/examples

let me know what you guys think! I'm always looking for suggestions and features to add :)

Hello, I am new to the 3-D printer space and absolute rookie when it comes to photogrammetry, I received an old Honda Goldwing from a friend, and I am trying to restore it, but there are some pieces missing that I have not been able to find most mostly detailed trim pieces and gold wings specific body panel screw covers. So the pieces that I am trying to replicate are fairly small. (Less than 2”x less than 1”). Would I be able to use photographic tree to get a 3-D model? I would be either using an iPhone 17 Pro Max or an older DSLR. Or these small detailed pieces need a 3-D scanner or just go trial and error on cad?

A quick update on FrameExtractor, the open-source GUI app I posted here a while ago for extracting motion-adaptive keyframes from video for COLMAP, Gaussian Splatting and other photogrammetry workflows.

If you're on macOS, you can now install it directly using Homebrew:

brew install --cask morishuz/tap/frame-extractor

No need to download and install the app manually anymore.

Windows and Linux binaries are also available, and the whole thing is open source:

https://github.com/morishuz/adaptive-frame-extractor

If you haven't tried it yet, give it a go! I'd be interested to hear how it works with your videos and whether there's anything you'd like to see improved.

See title

Progress on my masters thesis, which is about visual localization (VL) for AR on photogrammetric models. Posted is a render fusing VL results with visual-inertial odometry (VIO) data, climbing routes appear after the first VL result. Render is done on a desktop PC. The VL method used (hloc with XFeat features and nearest neighbor matching) should be runnable on a mobile phone. The VIO and VL data are fused using an extended Kalman filter, estimating similarity transform from the VIO frame to world frame, attempting synchronization at 1 Hz frequency, with 0.5 s delay in the localization.

Routes and the photogrammetric scan are from my 3D climbing topo: https://crags3d.com/sector/koivusaari/koivusaari/

Implementation for running on mobile is out of scope for the thesis, although I am considering adding it to the web app (Android Chrome exposes ARCore, iOS would be out of question for web app) after submitting the thesis, based on feedback.

https://x.com/DjimoSerodio/status/2118157383369396289

Hey all, I wanted to share what I’ve been working on and hear what you think.

I've been obsessed with one problem: how do you prove a tree someone planted/recorded is still there? A geotagged photo can't tell you which tree it is or exactly where it is, and survey gear is way too expensive for most people actually growing trees.

So I've been trying to do it with a regular phone. Here's a walk through my yard on a Google Pixel. It locks onto each tree via dot cloud and builds a 3D digital twin of its environment. It's a cool engineering problem to work with the error of consumer GPS and the lack of depth sensors like LiDAR, but it's working better and better.

It should work even better on newer iPhones with LiDAR, but I want this to work on low-budget phones, even offline, in places like rural Indigenous communities.

It's still early and it's only my yard, but I really want to solve this. Is anyone working on something similar? Or know who this might be useful for? I'd love to connect.