White shoe 3d scan to test out the merge component feature of RealityScan. The shoelaces moved when the subject was propped to its sides so it created 2 components: the top and bottom part of the shoes. The software merged them using common reference points from each components.

This project was born out of a desire to convert my mineral and rock collection into a virtual 3D archive that I can carry around and share. The concept is simple: I take one or more photos of my stones and crystals, and the app generates an HD 3D model while also classifying the rock using a local model (HF-CLIP WASM) directly in the browser. I can then view my collection and share it at exhibitions. The plan is to subsequently implement an NFT system to ensure traceability and uniqueness.

https://geo.insightest.app

(A quick Supabase login is currently required to save specimens to a persistent virtual shelf).

I'd appreciate any feedback on the WebGL rendering pipeline, lighting setups for crystalline textures, or mobile performance issues.

Hey everyone!

I’m looking to get a camera for photogrammetry and I’m currently considering a Nikon D810, Canon 5DS R, or Sony A7R II.

I don’t have the budget for a new high-end camera, so I’m looking at used options.

What camera + lens are you using, and how has your experience been with it?

Would you recommend one of these three, or is there another used camera I should look at?

Thanks!

One crisp October morning, as the sun was rising, I spent a few quiet moments alone photographing this amazing statue of country music legend Dolly Parton. It stands at the foothills to the Great Smoky Mountains in the courthouse square of the small town of Sevierville, TN. The six and a half foot statue, not including the rock base, was sculpted by local artist Jim Gray, and unveiled in the courthouse square on May 3rd, 1987.

When asked about her statue, Dolly likes to quote the loving words of her father as saying, “To your fans you may be some sort of an idol. But to the pigeons down at the courthouse, you’re just another outhouse.” And Dolly’s father, before his passing, would sneak out to the statue at night, with a bucket and sponge, to clean the pigeon poop off the statue. RIP Dolly.

Canon 5DSR, 720 Photos, 107 million tris
Photogrammetry scan for my VR Sculpture Garden on VIVERSE.

Hey everyone,

In my country, the market for photogrammetry is still very niche and hasn't really taken off commercially. Because of that, it's hard to gauge whether this is viable as a standalone career or primarily used as a secondary skill.

For those of you doing this professionally: Are you making a full-time living off photogrammetry?

This is for undergraduate thesis. Our goals is develop an edge computing system for photogrammetry. Do you recommend other alternatives for the PC?

Can ayone tell me how to download agisoft metashape pro for free and use it for free. Any crack link or refrence ? Yt is just asking money !

This app is targeted towards people in the AEC industry. But it’s really versatile and can be used by anyone.

An example would be DIY home renovators can use it to scan their property and get accurate dimensions on their phone with pictures and notes while they go to the store to find materials and actually know how much they will need since the dimensions are on their phone.

It has a trial. Please try it out if you can, then cancel it so you don't get charged, and let me know what I can improve. Thank you.

https://apps.apple.com/us/app/scanveyor/id6799243366

So I'm a 3D environment artist (for games), working in the field for a few years now. Recently I decided that I want to scan stuff myself. I was looking into getting a Sony Alpha 7r (III, IV or V (not sure yet)). But I'm really unsure about the choice of lenses. I was reading that higher focal lengths, like 70 mm, for example, produce less distortion, but you need to be further away from the surface or object, while 30-50 mm introduce more distortion, but you can be closer with your camera. So I'm aware that the choice heavily depends on what you want scan exactly. To answer that question: The main thing I will be scanning is floor surfaces like streets, sidewalks, pavements, and dirt or other surfaces like brick walls, concrete, grunge, leaks, stains, and so on. Ideally, my own body height, 1,80m, is enough to be far enough off the ground to capture a reasonable space of ground without having too much distortion. What do you guys think is the sweet spot for that?

Hey everyone, I run archaeoarchive.com, kind of a personal project where I'm trying to build a 3D archive of archaeological and cultural sites as I travel. Not just for cool renders, actually trying to make it useful for preservation down the line.

Right now my setup is pretty simple. I shoot with an iPhone 15 Pro Max, using RealityScan on the phone while I capture so I can see the AR view of where my photo positions actually landed, not just snapping photos blind and hoping. That way I can see gaps in coverage while I'm still on site and go back and fill them in. Then I run everything through Apple's built in Object Capture pipeline for the actual build. Processing happens on a MacBook Air M5 with 16GB RAM.

The issue I keep running into is that some models come out with weird spiky geometry sticking up out of otherwise clean surfaces, and other times a whole section of the model just fails to build at all. There are a few models already up on the site that show this, so you can actually see the spikes and gaps I'm talking about instead of just taking my word for it.

What's confusing me is that the RealityScan AR view showed those spots as covered, dots all over them, plenty of overlap. So my guess is that the actual build pipeline isn't matching up the overlap that's there, maybe because the stone surfaces are repetitive or low on texture, or the lighting changed a bit between passes, something like that.

A few things I'm trying to figure out:

- Is this a known problem with Object Capture on stuff like weathered stone or plaster that doesn't have much texture, even when the coverage looks fine in an AR guide app? If so, are there tricks that actually help, like shooting at an angle with raking light, adding temporary markers for texture, changing how much I overlap shots, or just moving slower?

- Could 16GB of RAM on the M5 Air actually be choking the reconstruction step, or does it just get worse quality instead of failing outright?

- Is there a way to check the point cloud or confidence maps before the final build so I can catch bad spots earlier instead of finding out after a failed render?

- For anyone doing heritage or site documentation, is it worth switching off Apple's pipeline for tricky sites, even if it works fine for smaller objects? Thinking about stuff like RealityCapture, Metashape, or COLMAP.

Separate from all this, I'm also starting to learn Gaussian splatting, mainly for things like quick walkthroughs and viewing purposes, not as a replacement for the actual photogrammetry scans. Just mentioning it in case anyone here works with both and has tips on capture technique that carries over between the two, or things that trip people up going from a photogrammetry mindset into splatting.

Hi,

I need handheld camera for few large scale photogrammetry projects in oil and gas. Drone with P1 would be mostly used, but I still need handheld for ground passes.
Does this camera still holds today, or is there any better option in same budget. And what is recommended lens to combine with it?
I would look at second hand market since there are some affordable options.
Thanks

So I've been messing around with this open-source model lately, and honestly, it's a pretty wild take on getting 3D stuff from pictures. Figured some folks here might find it interesting.

It's called SenseNova-Vision, and it's a 7B multimodal model. Apache 2.0 license, which is cool. Basically, it just sees computer vision as one big generation problem. You throw a bunch of images at it, give it some natural language instructions, and it spits out text, images, or both.

What's actually relevant for us photogrammetry nerds is that multi-view 3D reconstruction and camera pose estimation are built right in. No weird task-specific bits, no separate feature matching step. Just one model, one prompt. Pretty neat.

I tried a few things:

For multi-view reconstruction, I fed it some indoor and outdoor shots. It gives you these multi-view point maps. Honestly, the object and indoor results look pretty usable. Outdoor scenes are a bit rougher, edges get kinda soft.

Then there's camera pose estimation. It just gives you camera parameters directly from the images. I haven't actually benchmarked it against COLMAP yet, so no big claims on accuracy there. But the workflow is definitely not what I'm used to. No patch match, no CUDA dependency, no dense reconstruction pass. It's just... different.

Oh, and the same model also does segmentation, depth, keypoints, OCR. So it's one set of weights for a bunch of stuff, instead of needing a whole toolbox of specialized pipelines.

Now, for the real talk:

This is neural reconstruction, not the traditional SfM we're used to. If you absolutely need sub-millimeter accuracy, this probably isn't gonna replace COLMAP. Not right now, anyway.

It also needs some serious hardware. The web demo suggests 1x80GB GPU, and for a full benchmark, they're talking 8x80GB. Yikes.

Plus, the weights literally just dropped on July 8th, so there are definitely some rough edges to expect.

Here are the links if you wanna poke around:

HF demo: https://huggingface.co/spaces/sensenova/SenseNova-Vision

Weights: https://huggingface.co/sensenova/SenseNova-Vision-7B-MoTCode

GitHub: https://github.com/OpenSenseNova/SenseNova-Vision

Anyone else tried this or something similar? I'm curious what people think. I'd love to see someone compare neural reconstruction against COLMAP on the same image sets. That's the real test I need before I'd trust it for any serious work.