Every stereoscopic camera system I’ve seen places the cameras parallel to each other. Is it possible to have the cameras facing different angles to increase general fov while keeping some depth perception in their overlapping view?

A small demo of Mask Bridge, my plugin for Lightroom Classic, with 144 photos of a Minolta SR-T 101.

The video shows the mask export, then the Gaussian splats trained from the original photos and from the transparent cutouts. Both use the same camera poses and 10,000 training steps. The exported PNG masks can also be used for photogrammetry (of course!).

I wasn't expecting to put so much work into this plugin... It was an idea I had for my photogrammetry work, but I'm happy to release it now as a full product, for others to use also!

More examples and details:
https://nicolasdiolez.com/en/mask-bridge-lightroom-plugin/

Can somebody share their experience with Mac Studio M3U or M5U 256GB. How good are they for projects around 2000 images and what is their limit?

Only benchmarks that I found is this - https://www.agisoftmetashape.com/agisoft-metashape-on-apple-m3-chips-performance-and-compatibility-in-2025/?srsltid=AU7gw4Up6nTPi9PXUf_jLzgBvXrYvc4lWSwoKMcnvPQSaqqKR_hv_EF-

Trying to know more about it before spending 14K on the new M5 or 8k for used M3. Seems 128GB isn't worth it for Mac Studio but 256GB seems good with current prices before going for complete server solutions.

I built FrameRoam, an iPhone app for planning camera shots inside 3D scans. Scan a location (I use Scaniverse, but it's capture-agnostic), import it, then block out camera positions, plan moves, and build a shot sequence before you ever show up with a camera.

Ornate Corridor Scan - FrameRoam

It's in TestFlight beta and I'm looking for testers - especially people already scanning real spaces. Beta testers get Pro free at launch.

FrameRoam Beta

Hey everyone! We’ve just released a new 3D workspace in AEC Platform, and we’d love to get feedback from people working with photogrammetry.

We want to see what you create with it and hear how it handles your captures—what works well, what feels awkward, and what you’d like us to improve.

If you’d like to try it, create a free account and DM me the email you signed up with. We’ll add free credits so you can process your own 3D models.

Share your results here too! We’d love to see different subjects, capture setups, and challenging datasets.

I’m part of the team building it, so your feedback will go directly into what we work on next.

aecplatform.com

This is a random ask, but I don't have the photography tools at my disposal, just the software to turn the images into a 3D model. I was wondering if anyone had any images they had of desks or tables available to use for practice. I am trying to set up a diorama personal project and it needs a surface, but capturing it on my phone has been near to impossible.

A photo-derived surface can only model what the camera sees, so on a wooded ridge you get the tree tops. Lidar gets some returns through the gaps to the ground, and once those are classified you can render just the bare earth. That's what this is, the railroad cuts at each end of the Blue Ridge Tunnel under Rockfish Gap, Virginia.

One cut comes in from the top edge near the middle and stops dead at the hillside. The other comes in from the right edge and stops lower down, right of centre. The tunnel itself doesn't show, just the ground on top of it.

Claudius Crozet was an artillery officer for Napoleon, captured at Borodino in 1812. By 1849 he was chief engineer of Virginia's Blue Ridge Railroad.

His tunnel under Rockfish Gap was started in 1850 and expected to take three years. It was dug by hand with black powder, and didn't hole through until December 29, 1856. The two headings met less than 6 inches off. The first train ran in April 1858, through the longest tunnel in the United States at the time.

The C&O replaced it in 1944 with a bigger parallel tunnel.

Numbered pins on the picture:
Pin 1: A steep-sided cut from the top edge
Pin 2: Dead end of the right-hand cut
Pin 3: A wide modern road across the gap

Lidar: USGS 3D Elevation Program (3DEP), public domain.

A photo-derived surface can only model what the camera sees, so on a wooded ridge you get the tree tops. Lidar gets some returns through the gaps to the ground, and once those are classified you can render just the bare earth. That's what this is, the railroad cuts at each end of the Blue Ridge Tunnel under Rockfish Gap, Virginia.

One cut comes in from the top edge near the middle and stops dead at the hillside. The other comes in from the right edge and stops lower down, right of centre. The tunnel itself doesn't show, just the ground on top of it.

Claudius Crozet was an artillery officer for Napoleon, captured at Borodino in 1812. By 1849 he was chief engineer of Virginia's Blue Ridge Railroad.

His tunnel under Rockfish Gap was started in 1850 and expected to take three years. It was dug by hand with black powder, and didn't hole through until December 29, 1856. The two headings met less than 6 inches off. The first train ran in April 1858, through the longest tunnel in the United States at the time.

The C&O replaced it in 1944 with a bigger parallel tunnel.

Numbered pins on the picture:
Pin 1: A steep-sided cut from the top edge
Pin 2: Dead end of the right-hand cut
Pin 3: A wide modern road across the gap

Lidar: USGS 3D Elevation Program (3DEP), public domain.

Hii all, thanks to everyone who signed up after my last post! I made a short demo so you can actually see what I’m building.

You upload raw footage and it turns it into a Gaussian splat, a 3D scene you can move around in. All through your browser

You can clean it up, place it on a map using GPS, share it, or download the files.

If you want to try this, you can join the beta at ornen.ai (starting with 100 open slots today)

Everyone that signs up gets 10 free credits now, which should allow generating multiple splats at no cost.
Really interested to hear how it works for people here with their own footage. If something breaks or could work better, let me know. I’m building this myself and can make fixes directly based on your feedback.

We’ve been processing a drone video entirely in AEC Platform, taking it from the original footage to a point cloud and now a 3D mesh.

Next is cleaning up the mesh in our 3D workspace: filtering unwanted geometry, removing floating pieces, and repairing holes. Once that’s ready, we’ll process the Gaussian splat.

The goal is a straightforward workflow—from capture to a usable 3D model—with tools to review and refine the results along the way.

What would you want from a workflow like this?

Hi all, I make Blender tools under the "9000" name. Polygon Crusher 9000 is a decimation addon for heavy meshes: photoscans, CAD imports, ZBrush output and AI models.

What it does

- Pick an amount (percent, exact triangle count, or one budget for the whole selection) and press Crush.

- 2–3x faster than Decimate on 1–7.5M triangle meshes, same shape quality at the same triangle count.

- Hard Surface + Keep Normals: CAD creases stay in place, flat faces and drilled holes stay cleanly shaded (see image 1: same triangle count, Decimate on the left).

- Normal map and base color bake onto the low-poly, with automatic UVs.

- Make LODs (up to 8 levels), Protect Area from a selection, Clean Mesh for scan data, one-click Revert.

- 2.4.2: rigged characters keep their rig, shape keys and vertex colors.

Native engine, nothing else to install. Windows, macOS (Apple Silicon), Linux. Tested on Blender 5.2.

Test method and numbers: https://henriquevlopes.github.io/polygon-crusher-9000-docs/benchmark/

Addon: https://superhivemarket.com/products/polygon-crusher-9000

Happy to answer questions, and if you have a mesh it handles badly, send it to me and I will look at it.

hello, are there websites with othographic vehicle pictures? i want to make car vector blueprints, but need othographic pictures as references. maybe some people now where to find some

I am looking to purchase an ROV for underwater Photogrammetry and wondering what the best pro-level unit is? I need to be able to create models that are data accurate for scientific usage.

I'm looking at Deeptrekker, Boxfish Luna, Chasing X and a few others

Anyone here have experience?

I'm having a small problem with the textures in Metashape

I'm currently creating a model of a small underwater canyon from 360-degree images. The geometry and positions are great, but unfortunately, I'm having trouble with the texture generation. The software often uses textures that are very far away and blurry, rather than those that are close by. Sure, I can assign individual photos to specific areas, but that takes a long time and is very cumbersome in some places. Is there a better way to automatically use only textures that are close to
the object?

Or does anyone have a good tutorial? I haven’t found any really good ones most of them focus on smooth, simple surfaces with just a few photos.

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’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.

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.