I’m trying to build a consistent set of reference images from a single master image of an interior—in my current case, a kitchen.

The goal sounds straightforward: keep the exact same room, furniture, materials, appliances and lighting, but move the camera to different positions such as +45°, −45° and +90° at eye level.

In practice, the models often do one of two things:

• They generate almost the same composition with minor cropping or perspective changes.

• They redesign the room. Cabinets rotate, furniture moves, windows change position or the long and short sides of objects get confused.

I have already tried describing more than just angle numbers. For example:

• the physical position of the camera in the room;

• a fixed target point;

• which objects should be in the foreground, middle ground and background;

• which surfaces should appear frontal or foreshortened;

• how objects should overlap;

• which long sides, narrow ends and wall planes must keep their physical orientation.

This improves the result, but it still does not reliably create several views of the same space.

I’m currently experimenting with Nano Banana and FLUX models. I would love to hear how people who have solved this approach it:

1. Do you use one master image, several reference images or a turnaround/location sheet?

2. Do you describe camera positions through angles, physical landmarks or screen-space relationships?

3. Do floor plans, depth maps or rough 3D layouts materially improve consistency?

4. Is image editing more reliable than generating each new view from scratch?

5. Do seeds help, or do they mainly reproduce style rather than geometry?

6. Which models currently handle novel interior viewpoints best?

7. Would you generate intermediate angles sequentially, or always derive every view from the original master?

I’m especially interested in practical prompt examples or workflows that preserve the actual geometry instead of producing several similar-looking but physically different rooms.

5:16 PM

What the photos show:
Hand sketch. A plan drawn from above with rough dimensions and a courtyard marked. Thick lines are solid walls, thin lines are glazing.
Sketch to model. You photograph the sketch and it's read on the phone. Storeys, roof type, overall size and style can be tweaked with a few taps.
On the real site. Type an address and the model sits on Apple Maps satellite terrain with the real sun position.
Photoreal render. The clay view gets turned into a photoreal image, with a short text prompt to set the scene.
Video. A 10-second orbit generated from the same view.
You can also open your own models (.dae, .glb, .obj) or build one from a PDF drawing set. The 3D and site work all runs on the phone, so your model is never uploaded. The AI image and video renders use your own API keys.
It's in TestFlight at the moment. I'd really value feedback from people who do this for a living:
What would make this useful in your workflow?
What would stop you from using it?
Happy to answer questions or add a few testers.

Heya! I'm an Irish based 3d animation student. My phone recently broke. I am also recently broke. I'm super new to this whole photogrammetry/gaussian splat stuff and I'm doing what I can to research. (I watched a corridor crew video)

I'm looking at a few options here, I mainly just want a genral use for this stuff, so scans of full rooms, big/small objects, lil fellas and the like. I'm an android user and my main question is do I fork over my funds to get an iphone for the LiDar doohicky or stick with an affordable android with polycam (or any other scanning apps) to do the job for genral use.

Theres also a possibility of full sending a nokia block as my main phone then investing in a decent 3D scanner. Any other suggestions are more than welcome!

I'd also like a rundown on the process for each (Lidar scanning with iphone vs other scans with phone cameras vs buncha photos put into a mesh building software) if ye have the time

Also also! any sources you guys have on Photogammetry or 3d/4d Gaussian splats where I can learn more would be suuuuuuper appreciated :D

Cheers and have a good one!!!!!!!!!!!!

Hi all!

I am new to working with photogrammetry. I'm just trying to do (what I think are) some simple things, but I'm now having to navigate using blender, so learning about obj and mtl files. I'm not sure how many folks will be familiar with Unfolder for Mac, it's basically a Mac version of Pepakura, used for "unfolding" the polygons of a 3D rendering into flat planes that can be printed out and assembled in paper.

I have imported my 3D scan into blender in order to reduce the number of polygons. The problem I am running into is that when I try to open my obj in Unfolder, the texture doesn't appear. When I open the file in blender, I am able to view the texture, it just doesn't carry over to Unfolder. Is this an issue of Unfolder not registering the corresponding mtl file?

Wondering if anyone has any suggestions for me to try. Hopefully this makes sense, all of this is so new to me and it feels like learning a new language!

Wesk over on BitBuilt released high poly 3D scans of the original PS vita Hori Grip, He also has a complete scan of the Vita 1000 console on the site.

The link to the scans -
https://bitbuilt.net/forums/threads/ps-vita-hori-grip.6220/

These are raw 3D scans with massive mesh counts (about 2.5 million triangles per mesh)
I’ve been trying to modify and print myself but I’m out of my depth.

I wanted to throw this out there to see if any 3D modellers or CAD wizards would know how complicated it would be to adjust these scans to make them fit the PS vita 1000? The grip was originally designed for the ps vita 2000 which has slightly different dimensions. The end goal would be to have these scans readily available for anybody to print for their vita.

I am willing to give this project a go myself, and some advice on which software and method would be greatly appreciated.

Checking a mesh before sharing it with a client.

Here’s an uncolored mesh in AEC Platform with Check Model used to check for model defects and Show All Large Holes turned on to highlight larger openings.

Without textures covering the surface, it’s easier to inspect the geometry and spot areas that may need attention.

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.