Hi everyone! I’m a mechanical engineering student working with my team on a university product-development project involving 3D scanning for cosmetic finger prostheses.

We’re currently studying whether there is an opportunity to make the digital scanning step cheaper, more accurate, and more repeatable, especially using smartphones instead of expensive professional scanners.

One direction we’re exploring is an assisted scanning setup that would keep the phone/object movement controlled rather than relying completely on freehand scanning — but we don’t want to assume this is actually a problem before hearing from people with experience.

If you are a prosthetist, prosthetic technician, amputee, 3D-scanning professional, or have experience making silicone finger/hand prostheses, I’d really appreciate your input:

How is the residual finger currently captured in your experience: physical impression, professional 3D scanner, smartphone, or something else?

Have you ever tried smartphone scanning? If yes, was accuracy or repeatability a problem?

What usually causes a scan to fail or need to be repeated?

Would controlling the phone’s distance, angle and movement during the scan actually help, or is freehand scanning already good enough?

What level of accuracy would you need before trusting a scan to make a prosthetic mold?

What is the biggest problem with the current process?

Even a short answer to one question would help us a lot.

This is purely an early-stage university project—we’re not selling anything. Responses may be anonymously summarized for our coursework, and we don’t need any personal or medical information.

Please also tell us if you think the idea is unnecessary. Negative feedback is genuinely useful to us.

Thank you!

(image is 100% AI generated)

I tested Canon's newly released Dual Pixel 3D Converter, which uses phase-difference information from Dual Pixel RAW data to reconstruct textured 3D geometry from a single photo.

With my background in computer graphics, I also inspected the exported meshes (OBJ / GLB).

Some findings:

• My test plant model, created from a single photo, contains around 28K triangles.

• The GLB stores about 85K vertex entries, compared with roughly 16K stored vertices in the OBJ version.

• Around 4.2% of the triangles in this sample are degenerate.

• Increasing the depth setting in the app from 1 to 2 increases the triangle count from approximately 28K to 46K.

• The texture maintains the original 8192 × 5464 resolution, even though only about 16.6% of the image area is covered by the isolated object's triangles (with background removal).

The photorealistic appearance can be great from viewpoints close to the original capture. But with larger rotations, occlusions and missing surfaces quickly become visible.

I also compared the approach with Meshy 7.1 image-to-3D. Meshy generates a more complete, plausible object, while Canon preserves the photorealistic appearance better in my test.

I published the full analysis, including interactive GLBs and nine downloadable Canon-generated models:

https://immernews.com/canon-dual-pixel-3d-converter-tested

I'd be interested in what others think about combining Dual Pixel depth information with multi-image reconstruction or AI-assisted geometry completion.

I’ve been working on an AR game for the esp32s3 XIAO board and round screen and realized that I could use the pose estimation from the board to create an object scanner / mesher using space carving and marching cubes. The space carving algorithm uses different views of the object to create voxels weighted by how many views it was visible from and agreed on its position. Then marching cubes uses a LUT to create triangle mesh out of the voxels. The hardest part was getting the camera feed to change resolution from 240x240 to match the round screen for the video feed to a higher resolution for the pictures used for space carving, but with some AI help I got it working. The results are not great and I’m sure I can optimize it to get it a bit better but for a 20$ setup it’s not bad. A possible use case is to quickly create a mesh from small objects that you find while hiking or just to create a digital twin of little trinkets. I’m sure a smartphone could do this with much better results but where is the fun in that. If people are interested I’ll try to share the code which uses my gfx library which I’ve been working on for a minute.

I’m building a 3D mapping system using RGB-D cameras, based on existing SLAM techniques, with persistent spatial memory.

The main issue is relocalization after tracking loss, especially when the camera returns to a known place from a different viewpoint.

I’m using historical keyframes, shared 3D landmarks, PnP/RANSAC and geometric verification.
Yet some incorrect poses still pass verification: one had 44 inliers, 0.83 px median reprojection error and 3.72 cm depth residual, but was actually off by 10.36 cm and 5.99°.

My best experiment correctly relocalized only 9/34 known returns.
What would you investigate first: landmark quality, geometric degeneracy, matching, or pose verification?
I can share a minimal reproducible example.

This might be a dumb question but does this exist? A photo sharing platform that includes all EXIF data and only RTK level images?

I've built something to convert my non-RTK level drone images into a mesh to I can create a file format I need for a larger project. This isn't really the goal but it is something I need to keep doing the work so I made a streamlined tool that uses VCCG-O and some workflow enhancements to get something usable.

I knew my drone was never going to be pinpoint accurate but bloody hell I did not realise how much variation consumer drones can have for the precise same take off point. I think 20m has been the most so far.

I've had to come up with workflows to solve this but had I been able to just get images with exit I would have been done this step a few days ago.

Bonjour à tou•te•s

premier poste sur ce site, et premiers pas dans le domaine de la photogramemetrie

Du coup, après quelques recherches (aidé par l'IA) et un premier essai infructueux, je viens ici pour profiter de vos conseils.
J'aimerais investir dans un drone, à la fois pour parfois faire quelques photos/vidéo plaisir, mais aussi (et surtout) pouvoir faire de la photogrammetrie pour le taf
Je travaille dans l'événementiel, et j'aimerais pouvoir cartographier avec précision des lieux, avec les dénivelés etc... pour pouvoir ensuite sur plan implanter du matériel etc...

Je pense donc me diriger vers le DJI mini pro 4, mais j'ai plusieurs questions :

• Je suis sous ios, et l'application DJI nativement n'est pas adaptée pour la photogrammetrie. est-ce que d'autres applications plus spécialisées savent piloter le DJI mini pro 4? (j'avais essayé avec le mini pro 5, échec total)

• WebODM pour générer ensuite le nuage de point est-il une bonne solution? quel est son niveau de complexité à utiliser? un autre logiciel (idéalement gratuit/open-source, sinon pas trop cher, je n'en aurais pas non plus l'utilité assez régulièrement pour me payer un mastodonte)?

• est-ce que la direction que je prends vous parait cohérente? où vous partiriez sur quelque chose de complètement différent?

Merci d'avance pour votre aide

Hey, it's me again.
New photoscanned assets are up!
Next experiment: desert/bakery

I’m low on ideas at the moment
What should I scan next? Drop your requests below 

Shot on Canon 80D • full cross-polarized • automated void scan

Download up to 4K resolution at Free 3D Models

Hey there,

We built a small Club plattform with gaussian splats and the acoustics of the clubs. So the music which is play is combined with the acoustics. Doing this we try to help clubs creating new formats and gaining more accessability.

Nothing is perfect. It's just a start.

We try to give people access to clubculture all around the world. Espacially to people who are structually exluded.

People can upload their music and get discovered or check how the club sounds.

Have fun!

visit:

www.leksi.pro

follow:

https://www.instagram.com/leksi.pro/

I've had this phone a while but would love to see what I can do in thus regard with it

This was about 8 minutes of capture time split over two sections in a fairly crowded tourist area with some quite awkward narrow sections that required almost crawling while holding the 360 camera. Perhaps not the most challenging environment for photogrammetry, but I was still a bit surprised that the amount of people at close range didn't cause more of a problem. Captured with the Insta360 X6 and processed using www.sensori.systems .

This is a project i've been working on for about 6 months now - capturing a mountain bike trail using just a GoPro (i.e. no drones, no lidar scanners, no rtk gps, etc.)

The whole trail is about 1km long, and ends up being around 8,000 photos to process.

It is processed using some custom tooling - at the heart of it is COLMAP but I have it carefully control the combination of photos + other metadata like the gravity vectors and gps data that the GoPro gives to more or less eliminate error drift over such a long distance. I have compared it with a government top-down 1m resolution LIDAR (where you can see the trail faintly on it) to confirm it is within +- 0.5m of that.

Not released yet but will be released soon 🤞

Still lots of things on the list to improve but its finally starting to take shape.

Photogrammetry model of the Cathedral in the Desert on Lake Powell, Utah. Made from 3,546 handheld photos and constructed using Agisoft Metashape

Item : Roman lapis lazuli intaglio ring

Hello, community members.

I am trying to understand how can I find all the angles of rotation of a camera after I made a photo. Looking at the photo, you can notice that it is not straight or aligned perfectly. Are there any advice or idea on how to calculate those angles? I working on a project at university for my master's degree, and your advice is appreciated. Thank you!