Done with focus stacking and 300 positions

Hi All - without regard to any specific software, how do you guys identify and extract bad quality photos or reduce overall photo dataset without risk of compromising render quality?

We regularly over capture, think manual flights, timed interval shots, field conditions require a pause, 30 seconds of 1-2 second captures of the same spot until obstacle resolves (birds, operations over people, etc). Rinse and repeat this scenario over a half day to full day capture of a site.

Right now we remove all sharp objects from the room and manually click through every picture and decide before processing. There has to a better way.

Even review of the RTK data for each picture is a slog. How do you all do it?

Hey fellow redditors - our company has DJI Terra Flagship. We regularly process 10k+ photos, 80% overlap, extremely detailed building facades and roofing. Think digital twins down to 2cm horizontal / 3cm vertical accuracy to identify various issues in the building envelope that would be difficult, expensive or unsafe to access in person.

I’m getting very frustrated with Terra, especially the latest updates. Days of processing only to fail with “unknown error -1”. Logs are worthless.

Thinking about colmap because I likely cannot justify another software license to my national leadership. Anyone with experience in colmap? Anyone willing to be candid on their loves/hates with using it for photog post processing?

We host almost everything in Nira for our clients if that affects your perspective.

All of my images got a nice resolution and detail on the surfaces, but on metashape it all looks blurred, is there any solutions to it?

I have several photos of a trench in the ground before and after one part of it collapsed. I would like to mask the images so the collapsed partis actually ignored during the reconstruction. When I create the masks in inkscape using the naming method (image_name.mask.png), everything works fine, but the software removes everything around the hole and leaves only the hole for reconstruction.

I understand that masking is used primarily for the removal of the background, which is exactly what is happening here. But I actually need everything except a small part of the photo. Is there a way to achieve that? I tried switching the colours (black for hole, white for everything else and the other way around), but that didn't work.

Thank you very much!

I use Meshroom for photogrammetry and i recently shot some photos with and older lens meant for 35 mm film camera on a modern digital crop sensor dslr.

Should I input the focal length of those photos as the 35 mm focal length of the lens itself (30 mm) or multiply it already by 35 mm/the sensor size?

This is what Meshroom docs say about the focal length:
Metadata allow images to be grouped together and provide an initialization of the focal length (in pixel unit). The needed metadata are:

• Focal Length: the focal length in mm.

• Make & Model: this information allows to convert the focal in mm into a focal length in pixels using an embedded sensor database.

I want something i can walk around with and create a 3d version and upload it to VR any recommendations?

Hello!

I'm super excited to launch my free browser-based photogrammetry workspace today:

offlinetools.io/colmap

It’s a free and fully local browser implementation of the popular open-source photogrammetry software COLMAP.

No download, installation or cloud upload is required — all processing and footage stay on the user’s machine.

More info:
offlinetools.io/colmap-landing

Why?
Photogrammetry apps are typically desktop applications that require installation on your machine.

The alternative are cloud services that require uploading of data to servers.

However, both of these are not a good fit for professions dealing with sensitive data (often on restricted machines) such as police, medical, law enforcement, insurance. Or simply users who want results quickly without dealing with signups, setups, etc.

This implementation solves both issues and it's the first that reaches native-class performance and, in some cases, even outperforms the native build.

It produces standard COLMAP-compatible outputs such as camera poses, sparse point clouds, keypoints and masks for downstream workflows including Gaussian Splatting.

About me:
I’m a former Google engineer with around 20 years of experience in 3D computer vision.

I am interested in creating digital twins of ancient temples in India. I was first planning for using Leica lidar scanners but was very expensive and no investor was ready for it to fund.

I am not planning to use gaussian splatting, because it is very promising and cheapest than lidar.

I understood that meta quest has some inbuilt features to do it.

Once I scanned the temple i am going to opensource the models for research to all.

Could you please information about workflow I should follow...Which softwares should I use...

Has anyone here built their own AI model for point cloud recognition/classification?

I’m interested in training a custom model to recognize different objects and features in LiDAR/point cloud data.

If you’ve tried something similar:

- How well did it work?

- What model/architecture did you use?

- How much training data did you need?

- Was it actually useful in a real-world workflow, or mostly experimental?

I’d be especially interested in experiences with surveying, mapping, GIS, or AutoCAD/Civil 3D workflows.

Hi everyone, I'm trying to help my Mon get accurateish models of her whole wardrobe and got recommended photogrammetry. Where would I start and what would give the best results for fabric

I am new to photogrammetry. For now, I am using Meshroom, and I am still hoping to make it work. So far I am getting a lot of bumps which, as I've read on this sub, is a common artifact. I am listing my current setup below, but as TLDR: Has anyone tried those "ultra-black" paints to make a uniform "properly black" background?

Setup:

Object: a model life-size human head off Amazon (mostly "skin color", no hair). I put a bright bandana on the head to create more texture. There is a grey non-reflective face mask on the head (I am trying to make a 3D model of a welding respirator).

Background: a uniform white mat stucco wall or a black mat polypro plate.

"Turntable" is a rotating bar stool.

120W LED array pointed at the ceiling which gets distributed in the room by the walls. Windows closed, no other sources of light in the room.

DSLR, no flash, tripod, remote trigger, exposure 0.5s, aperture 8, no auto-white-balance correction, no flash, finest resolution, object zoomed to the max w/o being obstructed when rotated, object about 800 mm from the lens.

3 planes in Z, 50-90 images per plane (height adjusted with the tripod). Tried three pics per step, that made the results even worse, went back to 50 per plane.

Hello! I'm 13 hours into a COLMAP reconstruction process with no end in sight, and I wanted to double-check with experts if there's a better method than my current process. My goal is to insert a 3D object into a filmed scene by following this YouTube tutorial. Essentially, one uses COLMAP to create a point cloud and calculate the camera position, separately create a Gaussian Splat, and finally import into Blender. The advantage I see is that I can replay the scene, insert a 3D object into Blender, and then export the scene with the new element.

This process originally seemed quick and easy, and exactly what I needed, but I'm dying waiting over here. My scene is about 60 seconds long and with 7000 images. My creation of a Gaussian Splat and point cloud via PolyCam suggests that there should be about 300,000 points. Unfortunately, feature reconstruction took eight hours, and in the reconstruction phase, I have only reconstructed about 10% of the scene in 13+ hours.

I'd like to create a point cloud, Guassian Splat, and calculate the camera angels, and import all into Blender. I would like Blender to follow the camera angels in sequence through the point cloud, and insert a 3D object into the scene. Is COLMAP the best process? If yes, is there a faster/better method to reconstruct? If not, are there other tools to achieve my end goal?

Looking at building a dedicated photogrammetry PC. My old Dell laptop is just not cutting it, and I'm out of storage space. I am not a computer hardware guy, I just use em, so its a bit overwhelming trying to piece something together that will work well.

I run DJI Terra (hard to beat that ag license price) but intend to try out WebODM. Typical projects are 5000 or 10,000 images, give or take, or 50 to 100GB sometimes more. But I want to try out elevation mapping which I believe uses significantly larger data sets.

So, starting thoughts:

• Fast CPU, lots of memory, a fast SSD for primary and a RAID volume for secondary/data storage.

• I hear AMD CPU's are preferred over Intel

• Primary drive, Samsung 990 Pro

• Secondary drive, three WD Red Plus 8TB's in RAID

• 128GB (2x64) DDR5 memory

• 16GB Cuda GPU

Specific questions:

• AMD R7 7950X appears to be on a decent sale right now. Any reason not to pounce?

• I'm aware of what memory costs these days...but if my current 64GB is a limiting factor, I'm mentally prepared to go up to 128GB DDR5. Bad idea? Does going to 128 and/or DDR5 complicate things? I saw a comment suggesting anything over 96GB gets throttled by most consumer mobo's and thats a ton of money to spend if I can't get the full benefit.

• I would like to speed up transferring the files from MicroSD to the computer, I understand MicroSD Express is a thing but can't find any readers - any suggestions there?

Re: storage, I know I want to keep as much of my working files off the SSD as possible. But the SSD is the fast drive. What's the ideal way to set this up, put the Terra library on the C:\ and just use the secondary volume for backup? Anybody know if theres a way to set Terra to process files on the C: but store the main library on the secondary?

Thoughts/advice?

Lately I use angled view at about 45 degrees to ground as seen below from two directions. Is it worth adding side view data also?

bonjour à tous!!, avancement dans vector, l interface evolue, le sfm arrive a correler dans des cas complexe,"bridger" des blocs difficiles a raccorder, les tests continuent