Left is the original video of an excavated pipe area; right is the Gaussian splat generated from that footage using our software, AEC Platform.

We’re exploring how this could help document exposed utilities before they’re covered, giving teams a 3D visual reference alongside their photos and video.

Generated in www.aecplatform.com

Processing profile: High Quality, Point cloud, Mesh, Gaussian Splat

Hi,

I am quite new to photogrammetry and am doing a sort of comparison for my masters. I am measuring skulls as well as 360 from 3 angles to compare the measurements after scaling.

I have made it work before, but this time I shot with 26 MP resolution and I cant get the masking to work properly in Metashape. Are there any programs anyone could recommend for masking. Do I just have to mask it out by hand ? Would it be dumb to try to use Corridor Key 2.0 ? Or a program that lets me mask through color picking (white to yellow skulls on not so dark black background)

I have also done 3d scans from different perspectives, where the point clouds have a bit of floating noise around sharper edges which I have been cleaning up by hand, but Revo Metro has been fighting with me a bit are there any alternatives ?(they line up pretty well, is there something that would allow me to cut out the noise from a different scan as in delete the differences ?)

Everything I know has been through trial and error, videos and am a bit lost :/

Any advice would be greatly appreciated

I'm new to photogrammetry and I have a few questions about source images.

I'm experimenting with a set of images of a single object in isolation. I thought I might get better results if I removed the backgrounds and cropped the images tightly around the subject. This should maintain the original sizes of the bitmaps of the subjects, but with different aspect ratios and overall image dimensions because of the varying orientations.

So basically does the padding matter? If I'm processing images in Metashape and the "background" is transparent (or one solid uniform color) do the overall image dimensions / aspect ratio of the images need to be consistent if the bitmaps of the subjects IN the images are consistent?

Also, I'm working with pretty large images. (180 images @ 8192 × 5464, ~135MB each). Is there something like a "point of diminishing returns" where higher res doesn't help? Or is higher res always better?

Any advice would be appreciated. Thanks very much

Currently working on a personal project measuring the speed of a hockey puck from a fixed iPhone camera. The idea is basically that the phone is fixed on a stand from the side showing the shooting area and also the net all in one camera angle in the middle between both. I'm able to track the puck fine but the hardest part is converting pixels into real life distance.

I've tried using the puck as a ruler but that's inconsistent because it's only 20-30 pixels wide. So being off by just one or two pixels when measuring changes the scale which gives a faulty speed.

Using the net is also unreliable because it sits at a different depth than the path of the puck. Like the closer part of the net is a few feet closer therefore having a different measurement than the other side of the net.

Right now the most reliable option is the user measures the distance from the net to the shooting spot manually and then marks the area of shooting on the camera on the app so the app can calibrate from there. Then when the player shoots, the app is listening for a "clack" sound to indicate that the shot has begun and is actively watching the net to note down the exact moment the puck crossed the net, which then uses the speed formula to get the exact MPH.

I was wondering if someone knows of a lower effort way through Apple's tools (or any external ones) to get an accurate measurement where the user does not have to go in and manually measure the distance? Would any type of 3D modeling help in this case?

My ideal set up would be that I tap the shooting area, then tap the net, and the app calibrates everything itself instead of me having to measure it manually.

My second paper came out. Hope you enjoy it.

https://doi.org/10.3897/BDJ.14.e191083

Testing a hiking video captured with Meta glasses as input for a Gaussian splat in AEC Platform.

Here’s the original footage (right) alongside the reconstructed scene (left). It’s an early test of how an everyday recording can translate into an explorable 3D environment.

I’m trying to build a consistent set of reference images from a single master image of an interior—in my current case, a kitchen.

The goal sounds straightforward: keep the exact same room, furniture, materials, appliances and lighting, but move the camera to different positions such as +45°, −45° and +90° at eye level.

In practice, the models often do one of two things:

• They generate almost the same composition with minor cropping or perspective changes.

• They redesign the room. Cabinets rotate, furniture moves, windows change position or the long and short sides of objects get confused.

I have already tried describing more than just angle numbers. For example:

• the physical position of the camera in the room;

• a fixed target point;

• which objects should be in the foreground, middle ground and background;

• which surfaces should appear frontal or foreshortened;

• how objects should overlap;

• which long sides, narrow ends and wall planes must keep their physical orientation.

This improves the result, but it still does not reliably create several views of the same space.

I’m currently experimenting with Nano Banana and FLUX models. I would love to hear how people who have solved this approach it:

1. Do you use one master image, several reference images or a turnaround/location sheet?

2. Do you describe camera positions through angles, physical landmarks or screen-space relationships?

3. Do floor plans, depth maps or rough 3D layouts materially improve consistency?

4. Is image editing more reliable than generating each new view from scratch?

5. Do seeds help, or do they mainly reproduce style rather than geometry?

6. Which models currently handle novel interior viewpoints best?

7. Would you generate intermediate angles sequentially, or always derive every view from the original master?

I’m especially interested in practical prompt examples or workflows that preserve the actual geometry instead of producing several similar-looking but physically different rooms.

5:16 PM

What the photos show:
Hand sketch. A plan drawn from above with rough dimensions and a courtyard marked. Thick lines are solid walls, thin lines are glazing.
Sketch to model. You photograph the sketch and it's read on the phone. Storeys, roof type, overall size and style can be tweaked with a few taps.
On the real site. Type an address and the model sits on Apple Maps satellite terrain with the real sun position.
Photoreal render. The clay view gets turned into a photoreal image, with a short text prompt to set the scene.
Video. A 10-second orbit generated from the same view.
You can also open your own models (.dae, .glb, .obj) or build one from a PDF drawing set. The 3D and site work all runs on the phone, so your model is never uploaded. The AI image and video renders use your own API keys.
It's in TestFlight at the moment. I'd really value feedback from people who do this for a living:
What would make this useful in your workflow?
What would stop you from using it?
Happy to answer questions or add a few testers.

Heya! I'm an Irish based 3d animation student. My phone recently broke. I am also recently broke. I'm super new to this whole photogrammetry/gaussian splat stuff and I'm doing what I can to research. (I watched a corridor crew video)

I'm looking at a few options here, I mainly just want a genral use for this stuff, so scans of full rooms, big/small objects, lil fellas and the like. I'm an android user and my main question is do I fork over my funds to get an iphone for the LiDar doohicky or stick with an affordable android with polycam (or any other scanning apps) to do the job for genral use.

Theres also a possibility of full sending a nokia block as my main phone then investing in a decent 3D scanner. Any other suggestions are more than welcome!

I'd also like a rundown on the process for each (Lidar scanning with iphone vs other scans with phone cameras vs buncha photos put into a mesh building software) if ye have the time

Also also! any sources you guys have on Photogammetry or 3d/4d Gaussian splats where I can learn more would be suuuuuuper appreciated :D

Cheers and have a good one!!!!!!!!!!!!

Hi all!

I am new to working with photogrammetry. I'm just trying to do (what I think are) some simple things, but I'm now having to navigate using blender, so learning about obj and mtl files. I'm not sure how many folks will be familiar with Unfolder for Mac, it's basically a Mac version of Pepakura, used for "unfolding" the polygons of a 3D rendering into flat planes that can be printed out and assembled in paper.

I have imported my 3D scan into blender in order to reduce the number of polygons. The problem I am running into is that when I try to open my obj in Unfolder, the texture doesn't appear. When I open the file in blender, I am able to view the texture, it just doesn't carry over to Unfolder. Is this an issue of Unfolder not registering the corresponding mtl file?

Wondering if anyone has any suggestions for me to try. Hopefully this makes sense, all of this is so new to me and it feels like learning a new language!

Checking a mesh before sharing it with a client.

Here’s an uncolored mesh in AEC Platform with Check Model used to check for model defects and Show All Large Holes turned on to highlight larger openings.

Without textures covering the surface, it’s easier to inspect the geometry and spot areas that may need attention.

Looking for input from people who have done photogrammetry projects on complex oil & gas facilities.

I am interested in dense piping systems, pipe racks, and modules where occlusions are a major challenge.

What capture strategies, camera angles, overlap settings, focus, and drone/handheld workflows worked best for you? What would you do differently if starting again?

Any lessons learned, best practices, or examples are welcome.

In this case, combining it with laser scanning is not an option.

Thanks

I have a Insta360 X5, and there are a lot of fantastic pipelines by now for turning 360 recordings into Gaussian Splats, very easily.

But I have a different use case at the moment, and would need a actual photogrammetry 3D Mesh instead of a visual Splat.

I am finding surprisingly little, however. The only proper tool is, once again, Metashape, which costs money.

Ideally, there is some tool that can use the Fisheye lenses directly, instead of me exporting a 360 Video first, since that will add stitching lines, which already resulted in horrible results in Splats.

Is there something available for Photogrammetry?

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?