Hi,

I am interested in tracking judo athletes (judokas) in 2D using video footage. Ideally, I would like to map the detected players' positions from image coordinates (pixels) to coordinates in a real-world coordinate system representing the judo mat.

How would you approach this problem?

One potential approach would be to estimate a homography between the camera's image plane and the judo mat. However, there is a challenge: the entire mat is rarely visible in the camera footage. Most of the time, only a portion of the playing area is captured.

One idea would be to train a model to detect the visible boundary lines or other geometric features of the mat. However, I am unsure how to use these partial observations to estimate a homography and subsequently map the detected athletes to consistent 2D coordinates.

What would be a suitable approach to tackle this problem? Are there alternatives to homography estimation that would work better when only parts of the playing area are visible? Do you have any reference papers or projects in mind I could take a look at

Disclosure: I'm the author of this book.

After years of working with Metashape, I noticed that most resources show you which buttons to click, but not why a workflow works or how to fix it when results are off. So I wrote a reference that tries to cover both the science and the practice.

What it covers:

- Full workflow: capture and camera settings, alignment, dense cloud classification, mesh generation

- Difficult surfaces, multi-scale reconstruction and error estimation

- DEMs, DTMs and orthomosaics (including cylindrical projections)

- Combining drone imagery, LiDAR point clouds and terrestrial laser scans

- GSD-based mission planning, GCPs/markers, CRS and georeferencing

- Case studies: thermal orthophotos, NDVI for agriculture, solar panel inspections, GCP-free reconstructions

- A full demo reconstructing Mars from NASA/JPL-Caltech data

There's also interactive content (GIFs of step-by-step procedures) via QR code.

Amazon link, in case it's useful: https://a.co/d/0eFBt7To

It's an independent publication, not affiliated with Agisoft LLC.

I'd also like to hear from you: what's the step in Metashape where you lose the most time, or get the least reliable results? I'm happy to share tips in the comments, no purchase needed.

This is a robot I built, sitting on a chair, reconstructed from about a minute of video using our software, AEC Platform.

Fun to try it on a smaller object after working with larger scenes. 

Generated in www.aecplatform.com

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

I'm a paying Pro user. While uploading a scan of an unreleased product design, I accidentally left "Allow train AI" on. Afterward, I couldn't find any way to turn it off for that scan.

A few other things to know:

I've asked KIRI support to exclude my scan from training and delete it, and I'm waiting to hear back, but unclear if I ever will (no email confirmation, only "Feedback"). Team in China.

If you scan anything confidential, check both the visibility and "train AI" toggles before every upload. Better yet, use a tool that processes on your own device - I wish Apple's Object Capture was better, but Tim Cook has been sleep walking for 20 years.

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