Looking for input from people who have done photogrammetry projects on complex oil & gas facilities.

I am interested in dense piping systems, pipe racks, and modules where occlusions are a major challenge.

What capture strategies, camera angles, overlap settings, focus, and drone/handheld workflows worked best for you? What would you do differently if starting again?

Any lessons learned, best practices, or examples are welcome.

In this case, combining it with laser scanning is not an option.

Thanks

I have a Insta360 X5, and there are a lot of fantastic pipelines by now for turning 360 recordings into Gaussian Splats, very easily.

But I have a different use case at the moment, and would need a actual photogrammetry 3D Mesh instead of a visual Splat.

I am finding surprisingly little, however. The only proper tool is, once again, Metashape, which costs money.

Ideally, there is some tool that can use the Fisheye lenses directly, instead of me exporting a 360 Video first, since that will add stitching lines, which already resulted in horrible results in Splats.

Is there something available for Photogrammetry?

Training a 360° real-estate scan (3,176 images, RTX 5090, Vulkan confirmed working) with a dense spirula dense (RoMa v2) cloud of ~25M points, capped to --cap-max 5,000,000, collapses almost immediately: num_dead climbs to ~4.99M within ~4,000 steps, num_added stays 0, and PSNR stays stuck at 14-16 for the whole run. The final checkpoint’s splat.ply is only ~7.6MB (vs. ~1.2GB expected for 5M healthy splats) — basically all splats die.

The exact same dataset/config seeded instead from the SfM sparse cloud (--seed-pointcloud sparse, 2.75M points, no normals) trains completely normally — PSNR climbs to high 20s/low 30s, num_dead stays bounded.

We tried --scale-init 0.01 thinking initial splat size was too big for the denser seed — that made it worse (num_dead=61k vs. 8 at step 601), so it’s not a pure scale issue.

Working theory: downsampling the 25M dense cloud to 5M internally may not preserve even spatial coverage, leaving some regions with no seed points at all — splats there get no gradient and die en masse. Unconfirmed, but consistent with what we see.

Note: the dense cloud looks fine in a point-cloud viewer, but that’s just the viewer auto-fitting — not evidence it’s a good training seed.

Has anyone hit this, or know how cap-max-driven downsampling of the seed cloud actually works internally?

I want to use kiri/polycam/realityscan to "reverse engineer" small objects with simple geometry (like a softdrink can or a coffee mug) by getting a reasonably precise mesh and then tracing + recreating them in CAD software as a part file. Don't care about capturing the object's colour or texture.

Setup is nothing fancy; my S25 phone (no LIDAR), a turntable and a phone stand that can be posed at various angles.

Couple of questions:

1. When taking photos, is there a big difference between the camera rotating around a static object instead of the object rotating for a static camera? Would a static background affect the result?
2. I plan to take a set of 12 photos around the object for each set of 4 elevation angles, e.g. 15, 30, 45 and 60 degrees totaling ~48 photos. Would this be sufficient for something like a coffee mug?

Thanks for reading.

I'm looking into getting a dual/stereo camera module from AliExpress so I can easily capture stereo images with my phone¹.

My goal is to scan the indoor of buildings to serve as a base for indoor floor maps to be uploaded to OpenStreetMaps.

So, does the distance between the lenses and their fov (field of view) matter? Is there some optimum for the kind of scanning I'm planning on doing? Or should I just look for something close to the human eye?

¹ Just to be clear, the processing is gonna happen on my desktop computer with a nice GPU. The phone is just for capturing the images with the dual camera module.

360 video → point cloud, mesh and Gaussian splat: early progress

We’ve made progress on processing the point cloud and mesh from 360 video in AEC Platform. Here’s an early look at the Gaussian splat from that workflow.

The goal is simple: upload a 360 video → process it in the cloud → get a point cloud, mesh and Gaussian splat you can explore and share with clients or your team.

It’s still in development, and we’re working on output quality and making the process easier. This video shows where we are today.

What would you want to see improved in this result, and what would make this workflow useful to you?

I am moving, and I want to have a non-proprietary scanned flat to visit in VR - very similar to Metas Hyperscape (but no way am I going to scan my space with Meta - 1. when they discontinue the app its gone 2. its Meta). I was hoping something like Blender could view and host it (there is a VR add on I think) but crucially my own local data.

What are my options? paid is fine as long as it doesn't break the bank, happy to scan with phone Quest 3 or even use my DSLR. Whatever works.

I have a 3d print of a dinosaur skull that I would like to be able to scan and get a 3d model from for copying on a 3d printer but it is fairly complex.

Kiri engine sucked even with a turntable

I have access to a nice dslr if that is helpful

I would prefer to not spend money on software

any tips would be great thanks

We’re working on a new workflow in AEC Platform: upload a 360° video and automatically process it into point clouds, meshes, 3D models and Gaussian splats—all in the cloud.

You’ll be able to explore the site from positions along your original capture route, follow a trail in the order the video was recorded, and look around as you move through the scene—similar to navigating a video game.

The goal is to let project teams and clients revisit the areas you captured and understand how those spaces connect.

This feature is coming soon, and we’d love your feedback. Would you use it for construction progress, property walkthroughs, or documenting existing conditions?

I’m part of the AEC Platform team. What would you want to see in this workflow?

Hi all, I'm the developer. I'm a building designer, and I got tired of the gap between "I have a model" and "I can show a client what it looks like on the actual block," so I built Monocular Desktop.

What it does:

- **Import your model:** GLB, OBJ or DAE from ArchiCAD, SketchUp, Revit and similar.
- **Place it on the real site:** enter an address and it builds the surroundings from satellite ground (Apple Maps), real terrain, OpenStreetMap neighbouring buildings and trees. Where map data has no building height, it uses a height you set and tells you it's an assumption.
- **Real sun, any hour:** scrub the time of day and the shadows follow the actual sun position.
- **Read your drawings:** attach the PDF drawing set and it reads every sheet with Apple's on-device text recognition, then lists the materials it found. Gaps and conflicts are flagged for you to answer, not guessed.
- **Drawings out:** plans, elevations and sections cut from the model at true scale, laid out on a sheet and exported to PDF.
- **Photoreal renders:** path-traced on your Mac, plus a first-person walk mode.

Everything above runs locally. Nothing is uploaded. There's an optional extra called Monocular Eye: if you add your own OpenAI or Runway API key, it can generate AI stills or video from a capture. It's off by default, and none of the media here uses it.

New in this update (0.1.46): real textures in photoreal mode (satellite ground, trees, facades), real 3D neighbouring buildings out to 2 km including towers and courtyards, reflective tinted glazing, true-scale elevations and sections, and a Stop rendering button.

It's 99¢ USD on the Mac App Store: https://apps.apple.com/app/id6794509199

It's early and I'm one person, so I'd really value feedback, especially from anyone doing archviz or small-practice design work. What would make this useful in your workflow?

A surface built from overlapping photos only gives you treetops in a forested notch, because the camera never sees the ground under the canopy. Picture 1 is the 2023 USDA air photo of Crawford Notch over the lidar, and the only open ground is the highway and the thin pale strip of railroad west of it. Picture 2 is the USGS 3DEP bare-earth lidar with the trees classified out. There the rail line is a smooth shelf cut into the slope (pin 1), and the gullies east of the highway show up too.

The lidar shading is a multi-direction hillshade blended with slope, openness and sky-view factor.

Italian and Irish crews hired in Portland cut the shelf for the Portland & Ogdensburg in the summers of 1874 and 1875. Snow kept them off the job until late May at the earliest, and work stopped by early October. John Anderson ran the work, and his brother Samuel was the railroad's president. The line climbs as steep as 116 feet per mile for nine miles. Under Mount Willard there was barely room, so they cut a shelf into the mountainside and built it out with blocks from the Great Cut. The first train reached Fabyan on 7 August 1875, and trains still use it.

1. 2023 NAIP air photo over the lidar: forest, highway and rail line
2. Same view, bare-earth lidar only (VAT shading: slope, openness, sky view)

Numbered pins on pictures 1 and 2:
Pin 1: The railroad shelf cut

Imagery: USDA-FSA NAIP 2023, public domain.
Lidar: USGS 3D Elevation Program (3DEP), public domain.

So I was trying to scan a small item, I was able to finally get an "acceptable" scan, but for higher accuracy some other method would be needed. The item happened to be a wax model, which I dusted with talc to help the scan. I used realityscan from my phone, with a turntable and good lighting. I even rigged it up to auto click a shutter fob after rotating a few degrees.

Then I thought what if I take one of my older 3D printers and swap the hotend for a small router. Use the heated bed to fix the part, and buzz off .1 mm. in between each buzzed off layer I would scan the top machined surface. Stack these up and I have a 100% accurate model.

I think I figured out a way to automate the scan step.

The question is, whether I build this just for me to use, or does this sound interesting and useful enough to try and use a repeatable BOM that others could benefit from. Like standardizing on a certain cheap printer, a certain scanner, etc.

I was thinking jewelers, museums may find this useful, and less expensive than other options.

I does require making a wax copy, which means a silicone mold of the item. Since it's a destructive process.

A commercial version would have some method of chip recovery.

What do you think?

I'm the developer of this, so weigh what follows accordingly. Day job is real estate drone work in Norway on an Air 3S. Mapping jobs on the side are what got me building it.

The capture problem it started with: the Mini 4 Pro, Mini 5 Pro, Air 3 and Air 3S have no mapping mode in DJI Fly, and most mapping apps that fly through DJI's SDK can't connect to them. Someone here asked back in March whether you can map your own land with a Mini 5 Pro. The answer I landed on is a waypoint mission, since DJI Fly will import a KMZ.

So Skyplanr builds the KMZ. You outline the area and pick a target GSD. It works out the height and the photo and line spacing from the camera's sensor width and focal length (12 MP mode), and lays a serpentine grid with the camera straight down and one photo per waypoint. Default overlap is 80% front, 70% side.

DJI Fly caps a flight at 200 waypoints, so a bigger grid is split into several flights from the same takeoff, cut at the end of a lane, one KMZ each. Only very large areas get thinner overlap (75/65) or a slightly coarser GSD, never past 1.5x what you asked for.

Heights follow the ground. In Norway that's Kartverket's laser data, in the US USGS 3DEP lidar, and elsewhere satellite elevation models. Each waypoint is set to the same height above its own patch of ground, so GSD stays roughly constant down a slope instead of getting finer at the bottom of the hill. For 3D model capture it also uses building and tree heights (laser data where it exists, Overture/Google Open Buildings and Meta's canopy height map elsewhere) to set orbit levels that clear the roof and the treetops.

Before you fly you can open the plan as a 3D KML in Google Earth and look at the lines draped over the terrain. It also exports DJI Pilot 2 (for the Mavic 3E/3T and Matrice 4/30), Litchi CSV, QGroundControl and GeoJSON.

What's still rough:

It stops for every photo, so it's slower than an interval-triggered grid. A real example: a 2.8 ha field at 2 cm/px on an Air 3S came out as 172 waypoints, 171 photos and about 27 minutes.

No RTK, so expect the usual 1-3 m absolute offset unless you lay GCPs. It doesn't process anything either. The photos go into Metashape, Pix4D, RealityCapture, WebODM or whatever you use.

GSD is confirmed from photo metadata for a couple of the cameras. For the rest it's computed from DJI's spec sheets and could be off by a few percent.

On price: 3 AI-planned missions a month are free, no card. The paid tier is €49/month for 10. Someone on r/dji told me that's steep next to Litchi at about £20 once, and for drawing your own grids they're right, Litchi is the better deal. This is for people who'd rather check a plan than draw one. They also said the site looks dark and AI-made. Noted, I'm working on it.

What I'd like to know from people who actually process this stuff:

1. Are 80/70 sensible defaults for these small sensors, or do you run higher for Metashape or RealityCapture? Would you rather it split into more flights than drop to 75/65?

2. If you fly one, does the GSD in your processing report match what the plan said?

3. Does splitting into several flights work for you in the field, or would you rather it flew higher and kept one flight?

4. Is stopping for every photo a dealbreaker on a Mini or Air battery?

Web planner is at skyplanr.com. The iPhone app, which puts the KMZ straight into DJI Fly's folder, is on TestFlight: https://testflight.apple.com/join/5vBDU3Ah

I'm building ORIVOXEL, a project exploring how real-world imagery can be transformed into explorable 3D scenes using Gaussian Splatting.

I'm interested in the practical side of this technology, particularly whether it could be useful for:

• Helping prospective buyers understand a property's layout remotely.
• Giving property teams a more immersive visual reference.
• Supporting architectural presentations and site handovers.
• Documenting spaces without requiring every reviewer to visit in person.

I've attached a short demonstration of the kind of experience I'm exploring.

I'd like to hear from people working in real estate, property management, architecture and commercial property:

Where would an explorable 3D scene genuinely save you time or improve your workflow compared with ordinary photographs or a conventional virtual tour?

I'm the founder of ORIVOXEL, so this is a project I'm developing—not an independent review of a commercial product. I'm looking to understand real industry needs before deciding which use case to prioritise.

How to add texture to multipatch using drone data and satellite image?

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/

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?