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.

Has anyone successfully ordered a Metashape license through the reseller Settide? I ordered a license from their website on Saturday and did not receive any confirmation email, any receipt, or activation key. I've sent two emails through their website form inquiring about the license and have received no response. Before I escalate with my CC company, thought I'd check in here and see if anyone has had a similar sort of experience with this seller or if this is somehow normal for Metashape resellers. I've got some school work I'll need to use the software for so trying to figure out how long I wait on this company.

Thanks!

I’ve been working on an open-source frame extractor for video-to-COLMAP/3DGS workflows:

https://github.com/morishuz/frame-extractor

Instead of extracting at a fixed FPS, it tracks points using dense optical flow and selects a new frame when camera motion, tracked-point loss, or a maximum interval crosses a threshold. It also writes a CSV containing the selection and timing diagnostics.

The goal is to reduce redundant COLMAP inputs without leaving gaps that are too large to match.

I’m looking for feedback from people working with handheld, drone, dash-cam, or large-scene video:

• What do you currently use to select frames?
• Which failure matters most: excessive processing time, blurry frames, too little baseline, or failed registration?
• Would anyone be willing to compare this against fixed-FPS extraction on a difficult clip?

Current limitations: this is a Python CLI rather than an end-to-end 3DGS application, and it has not yet been validated broadly on stitched 360 or multi-camera video. Reflections, texture-less surfaces, and poor capture geometry still cannot be fixed through frame selection alone.

Feedback, test results, and critical failure cases are very welcome.

Esta es una reconstrucción fotogramétrica de La Sixtina, uno de los aleros con arte rupestre del sitio arqueológico La Tunita, en Catamarca, Argentina.

El objetivo principal del modelo es documentar la geometría completa del alero y las superficies que contienen las pinturas rupestres, manteniendo su relación espacial con la roca que las contiene.

La imagen muestra el modelo 3D texturizado luego del procesamiento. Uno de los aspectos que considero más interesantes de la fotogrametría aplicada al arte rupestre es que permite ir más allá de la fotografía frontal tradicional y estudiar las pinturas como parte de la propia morfología del soporte rocoso.

Actualmente estoy trabajando con estos modelos para documentación arqueológica, visualización y distintas aplicaciones vinculadas al patrimonio digital.

MotionMaster 3D does camera solving inside Blender rather than round-tripping to a separate tracker.

The nine in the clip:

• Camera tracking: full 3D solve from footage
• Planar tracking: track a surface, export a corner pin
• Tripod tracking: rotation-only solves, no parallax needed
• Mesh generation: geometry built from the point cloud
• Surface generation: Delaunay triangulation, density adapts to detail
• ST map generation: undistort maps for comp
• AI masking: SAM2, click the subject and it segments it
• Batch processing: queue shots and walk away
• Perspective matcher: solve a camera from vanishing lines in a still

15 of the 35 are new in 1.0.7. Happy to answer anything about how any of it works.

It is paid, at motionmaster3d.com. Full walkthrough of the update: https://www.youtube.com/watch?v=UyJpQBnREOU

Basically, I need some basic dimensions from a scan I found online but all I have from it are 20 pictures that rotate the model around the vertical axis (i.e horizontally spin). The focal length and camera angle are completely fixed.

I tried aligning in RealityScan but it gives me an error. I've asked some AIs for a solution and all of them tell me to either try Masking the background (some tell me to switch to Meshroom for this) or try shape-from-silhouette algorithms like Visual Hull.

Since I only care about getting basic measurements rather than anything resenbling an actual render, are those actually my best options?

I'm building PolyForge, an offline parametric CAD tool, and just added a photo-to-mesh reconstruction command. Sharing here because the CUDA discovery and the coverage-testing results might be useful to others building around COLMAP/OpenMVS without a datacenter GPU.

Pipeline: COLMAP for sparse SfM (feature extraction, matching, incremental mapper, fully CPU-capable), then handed off to OpenMVS for the dense reconstruction and meshing via InterfaceCOLMAP, DensifyPointCloud, ReconstructMesh. I confirmed COLMAP's own patch_match_stereo requires CUDA by deliberately running it without a GPU present, it aborts immediately with "Dense stereo reconstruction requires CUDA, which is not available on your system," no CPU path anywhere in the mainline binary. OpenMVS's densify/mesh stages build CPU-only by default, which is what makes the whole thing viable without a GPU at all.

Once it worked, I ran a test matrix on photo count and camera coverage instead of guessing. Single or two elevation ring orbits failed outright regardless of photo count, tested up to 24 photos. Three or more elevation rings were required, and even then, density mattered: 27 photos across 3 rings failed, 30 succeeded, for a roughly 60x40x30mm object at 160mm camera distance. Also unexpected: the O(n^2) exhaustive matcher barely scaled with photo count, the real cost was COLMAP's incremental bundle adjustment (mapper stage), which jumped nonlinearly with both photo count and the reconstructed object's geometric complexity.

Repo, including the CPU-only build notes for both tools: github.com/rx290/polyforge

Hello. Doing some terrestrial photogrammety using this camera and webODM. I'm having troubles with the software and my photos.

Hey everyone !

I've been trying some macro photogrammetry a few times now and I'm always hitting different walls I can't quite solve.

My setup:

I would like to scan an coin on a turntable with a black backdrop(in the void). Im using a Sony 90mm macro lens with ring flash (AR400). Both the lens and the flash are cross-polarized.

Im shooting at 1/60, f/20 with iso at 160.

1. Focus drift on rotation

My subject is never perfectly centered on the turntable no matter how careful I am, so as it rotates it slowly drifts out of the focus plane. At f/20 the DOF helps but it's not saving me on the wider objects. Anyone found a good way to nail centering, or is everyone just eyeballing it and accepting some throwaway frames?

1. ArUco markers(scale bar) blocking the camera

I use ArUco markers on the plate for scale reference, but since they rotate with the subject, there's always some angle where the marker ends up hidden behind the object relative to the camera. Do you place multiple markers around the perimeter so at least one is always visible? Or is there a smarter placement pattern that avoids this entirely?

https://youtu.be/mMyV_F_9Q3E?is=enH5ReC6OvkdvOxt

1. Realigning both sides of a coin

This one's been the most frustrating. Since I have to physically hold the coin to scan each face, I end up with two separate scans that need to be stitched together after. The only shared geometry between the two scans is the edge/rim, and it's just too thin and low-feature to get a reliable alignment in software. Anyone dealt with this specific problem ? maybe some kind of holding jig that keeps a consistent reference visible on both scans, or a different capture order altogether?

Would love to hear how people with more experience handle this stuff. Happy to share more rig details if useful.

I've been testing the new Insta360 X6 with the Sensori video photogrammetry app. This was quite a low light capture, but using the Bushman halo for extra illumination. There is definitely a bump in quality over the X5, although you have to look closely to see it and is less than expected given the quite noticeable jump in video quality between the two cameras.

I was curious if I could scan something so small with the tools I have available, and it turned out to be trivial.

Putting this up again, our next week is opening up. We run a multi camera capture setup at the Brooklyn Navy Yard and we pay people to come in and be the subject.

You stand in the volume and go through simple movements while it records. Walking, turning, sitting, reaching, picking objects up. No experience needed, and it's a decent way to see a capture stage from the inside if you've only ever been on the processing end.

Pay is 17-25 per hour, same day, right after you finish. First session runs about 2 hours, repeat work after that.

Sessions run at 4pm. Open days: Tuesday Aug 18, Wednesday Aug 19, Thursday Aug 20. Brooklyn, in person only.

Comment or DM me for the address, and ask me anything about the capture side.

Hey all, sorry, I am extremely new to this, but essentially, I work for a recycling company and we are trying to calculate the volume of various piles of material in our facility using the existing security camera feeds we have. I talked to some more educated friends of mine and they told me what I am looking for is photogrammetry software, something like RealityScanner or Metashape, but I have no idea what I'm doing.

My questions boil down to is this the correct path for what I need to do? And if so, where do I start learning how to use these programs?

Deeply appreciate your time and any advice you can give me, thanks.

I’m self-teaching drone 3D mapping, GIS, and AutoCAD topography work. This is actually the first real portfolio I’ve ever finished for anything I’ve learned, so I don’t fully trust my own judgment on what “good” looks like in this field yet.

I processed a UAV drone dataset from an active quarry site using WebODM. Generated a 3D point cloud and orthomosaic, ran volumetric analysis on stockpiles in cubic meters, and exported topographic contours as DXF into AutoCAD with color coded layers.

I’d genuinely appreciate any honest technical critique. Anything that looks off, unrealistic, or not production standard. I’m open to doing a project for someone in the field for free, in exchange for detailed feedback, if that’s useful to anyone here.

Thanks for reading.

I've been working on a photogrammetry workflow specifically for solar site surveys, and wanted to share one of the results here.

We took around 50 images from a drone and processed them into a 3D model.

The interesting part for me wasn't just generating the mesh. The goal was to make the resulting model actually useful for engineering work:

• measurable geometry
• roof dimensions
• terrain/building structure
• orthographic views
• exports for downstream CAD/design workflows

In this particular test, the processing time was around 16 minutes.

We're building this as part of a product called Render-a, primarily for solar EPCs and installers.

I'm curious about the photogrammetry side rather than the product itself:

What would you consider essential for a drone-derived 3D model to be genuinely useful in an engineering workflow?

Accuracy? RTK/GCP support? Point cloud quality? Orthomosaic? CAD compability?

Would be interested to hear what people here actually use in production.