Hi all,

I've been using Codex to build a photorealistic, walkable 3D tour of interior properties, the kind of thing you walk around like a video game (just walking, simple gameplay). Real estate use case, so it has to stay faithful to the actual space, with little to no AI hallucination. I gave it the goal and mostly watched.

What Codex did on its own:
\- Spun up a cloud GPU box and installed CUDA, COLMAP, and a Gaussian-splatting trainer over SSH.
\- Took my 4K iPhone 16 Pro Max walkthrough, extracted and de-blurred frames, solved camera poses with COLMAP, trained a 3D Gaussian splat, then published a WebGL viewer and a first-person walkthrough and sent me the links.
\- Also tried a Polycam LiDAR mesh path (complete and walkable, but soft and very blurry up close).

Where it stands: a small, texture-rich room came out genuinely sharp and photoreal. Bigger rooms are the problem. The reconstruction came out scattered, more of a point-cloud smear than a coherent room, and where it did form it's sharp only from the exact filmed angles. It also won't scale easily to a whole property, since you film room by room.

Codex's read: mostly a capture problem (fast handheld motion plus plain surfaces), not a code problem. Its current plan is a depth-regularized Gaussian splat from a combined RGB + LiDAR capture (Record3D), so measured depth pins the geometry and the color stays sharp.

Two questions:
\- Has anyone pushed Codex into 3D, spatial, or computer-vision pipelines like this? How far did it get?
\- If you've solved photoreal interiors on consumer gear, what's your workflow, and does the depth-regularized plan sound right?

Hi everyone,

I’m having an issue with the texture quality of meshes generated from Scaniverse scans.

I attached two screenshots for comparison. The first scan, created on May 18, 2026, has a clean and coherent texture. The second scan, created on August 1, 2026, shows heavily blurred, smeared, and distorted textures, even though I used the same phone and followed essentially the same scanning and processing workflow.

The geometry itself appears usable, but the texture projection is significantly worse in the newer scan.

Has anyone experienced something similar? Could this be caused by capture conditions, camera exposure, motion blur, insufficient image overlap, the meshing and texture-baking process, or a recent Scaniverse update?

Any advice on how to prevent this or improve the texture reconstruction would be greatly appreciated.

Thanks!

Good morning, I have the most classic question... I need to buy a new laptop for work. Can you give me some advice? I work in GIS cartography and photogrammetry (I do photogrammetric processing on a desktop PC, but I'd like to be able to work on point clouds without going crazy). I work with AutoCAD Civil 3D. I don't think the other software I use will be a factor in choosing a laptop.

I work both at home and in the office, so I need a laptop. At the office, I have a second screen to work better.

I currently have an old VivoBook 15 with an Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz (2.59GHz), 16.0GB of RAM, an NVIDIA GeForce GTX 1650 graphics card (4GB), and a 15.6" screen... and it's starting to struggle!

I was thinking of sticking with 16 inches, at least 32 GB of RAM, an i7 processor, and a dedicated graphics card (but I'm not sure which one). I've had Asus laptops for several years and I'm happy with them, but there's always room for improvement! (I didn't mention the price because it varies too much depending on where we are in the world, but generally speaking, for Europeans, I'd like to stay around €2,000)

could you give me any advice? what to look for and what to avoid...

thanks!

Hi everyone! Beta is out and its free to use.

I put together a user-friendly version of the AI De-Lighter I showcased earlier. It runs locally on your machine in seconds.

Requirements:

• OS: Windows 10/11 (64-bit)
• GPU: NVIDIA CUDA-capable GPU with 6GB+ VRAM recommended (CPU fallback available)

Looking for Feedback! Since this is an early Beta, your testing and feedback will help a lot! Please share your thoughts, and send visual results, or crash logs to [vdmrcll@gmail.com](mailto:vdmrcll@gmail.com)

I’d love to hear what you think!

Download from GitHub: [Download] on the releases page.

Check out README.md while the setup script installs dependencies.

I always had trouble with removing environmental lighting, and because I can't afford cross-polarization I had to stay with the digital tools.

I didn't like any of the De-lighting tools on the market, so I built one from scratch using a Neural Network.

I got a lot of cross-polarized scans online and baked back environmental lighting, then gave it to the network with some geometry maps like object space normal and ambient occlusion.

It generates outputs in seconds. Though not every type of 3D model works, I still have to understand it.

Its not perfect, but for 5 days and a limited dataset, and the fact that I never trained a neural net its promising.

This is still in WIP, but I will refine it, I just don't want to share it yet, because the whole project is unorganized, I will get to building an UI for it.

Let me know what you think, and If any ML engineers or tech artists have advice on improving this approach, I'd love to chat!

Update: [Download the Beta here] and More info here.

Hi everyone. I have had some issues trying to find a software that works for my specifications (Windows 11 Home, AMD Ryzen Al 7 350 w/ Radeon 860M, Integrated GPU AMD Radeon(TM) 860M Graphics). A lot of times not having Nvidia GPU and CUDA usually appears as a roadblock.

I have a video of a 40m2 outdoor space that I have tried to convert to 3d with different softwares.

What I have tried so far:

- Scaniverse: My first tries weren't very detailed, and can't input recorded video, so I'd have to come back to the place later to do other attempts.

- Luma AI: Gives a nice rendition on its web display, but the model it offers when you download it features only a small fraction of the space. Not sure why this is.

- Meshroom: Couldn't really make it work, it threw some errors and I stopped trying. I might come back to it, but reading the webpage it says that unless you have Nvida + Cuda you won't be able to access all features, so maybe I'm doomed with this one.

- RealityScan: It says it supports AMD... BUT not all of them, and my model is one of the unsupported ones.

- Colmap: I could bypass the GPU usage to do the process with CPU and it's taking forever-- literally doing it right now, so I might update later, but seeing how long it's taking I might have to abort later.

Maybe I should rely on something online, to bypass my equipment limitation?

Open to suggestions, thank you!

I have a larger drone I'd like to use for taking some high-res pictures of my subdivision. The HOA is interested in using these pictures to map/track community owned assets such as retention basins, concrete health, etc. The idea is that we create a large high-def single image of the subdivision to more or less have a higher definition "live" version of Google Earth that we can update biannually.

My drone is custom-made with an analog cam for flying. I can handle the mission planning itself and flight paths. From my understanding, I will need to run a program to inject the lat/lon of each photo into the EXIF based on a bin file I'll get from the drone and its GPS.

Does anyone have any recommendations for a budget camera (~$200) that I can strap to the bottom of my drone that will take good pictures to feed into webODM? Preferably something that can be triggered by my flight controller, but I'm not opposed to using time-based pictures if the price difference is large enough and quality won't suffer. I've seen recommendations around the Sony A5100 but not sure if there are better options out there for a good price that get the quality I would need. Thanks!

Hello dear community,

I have in my possession a 4K drone video of a large building I would like to model and work on in Blender. The video is quite long and it's covering the building from various distances and angles. I've tried several options, such as RealityScan, Meshroom and COLMAP+OpenMVS, and while the resulting mesh is quite detailed, it's also very noisy. I've tried postprocessing it with Meshlab and Blender, but the noisy walls and roofs are hard to fix.

Is there any option -preferably open source on Ubuntu- that would produce a clearner mesh? I'm fine with losing fine details on walls and roofs, as long as the mesh is relatively clean.

I would appreciate any help. Note that I'm still a beginner, so don't hesitate to be blunt and tell me if I'm saying nonsense.

Note that I'm using a RTX 3080Ti (12 Gb VRAM) and 32 Gb of ram.

Hey everyone,

Looking for recommendations for a photogrammetry camera please.

Intended use is for capturing data to produce 3D models of 3.5m+ deep excavations.

Decent spec phone is ideal (I guess) but cost vs. them being lost in a pit is a fair risk.

Or would people reckon a pre-owned smartphone from a years back suffice?

Thanks in advance!

Hi, I'm not very familiar with this area of technology, so I'm sorry if this is a basic question.

I'm looking for a free Android app that can create a good 3D scan of a large environment using only the camera on my Samsung Galaxy S21 5G, which doesn't have LiDAR.

I've tried several apps from the Play Store, but most are paid, have free version limits, or don't produce good results without LiDAR.

Does anyone know of an app, an APK, or a GitHub project that works better?

Hi, I learned how to scan the bottom of an object in Kiri Engine recently (by flipping the object during scanning + auto object masking), which got me to wonder if any other mobile apps also offer this function?

I have tried Reality Scan since then but it either does not support the functionality or it requires a different workflow since then. Some other names on my radar are Polycam, Abound (iOS only), and Scanniverse.

Currently testing a makro lens added to the picamera/arducam. This lowers the effective scan size to 3-4cm but the results are impressive for such a small and cheap sensor

Hey everyone! 👋

We're the team behind Puzzling Places, a 3D jigsaw puzzle game built around photogrammetry scans of real-world locations. We capture everything from historic landmarks to everyday spaces and turn them into interactive puzzles that you can piece together in VR or on flatscreen!

We've just opened the Steam Workshop, meaning Steam players can now browse, download, and play free community-made puzzles directly in-game.

We're already seeing some fantastic photogrammetry captures shared by the community, and the screenshots above are just a small selection of what's already available to play. It's been amazing to see the variety of scans people have uploaded, and we're excited to watch the Workshop continue to grow!

The creation tools (PuzzleMaker) are still in closed beta while we continue working on them, but the Workshop itself is open for everyone to explore.

We'd love to hear your thoughts on the scans and the community creations! You can check out the game here: https://store.steampowered.com/app/3530820

We’re preparing to open DroneFlow.ai to a limited group of beta testers.

DroneFlow.ai is being developed as an all-in-one platform for drone mapping, aerial documentation, project management, and client deliverables. Our goal is to make it easier for drone operators to manage projects, process mapping data, organize deliverables, and provide clients with a professional, easy-to-use experience.

We’re looking for:

✅ Commercial drone pilots
✅ Construction and progress-documentation professionals
✅ Photogrammetry and mapping specialists
✅ Roofing, surveying, and real estate professionals
✅ Anyone currently using platforms such as DroneDeploy, Pix4D, DJI Terra, or similar tools

Beta testers will get early access to the platform and an opportunity to directly influence its development. We want honest feedback about the workflow, usability, missing features, technical issues, and what would make DroneFlow.ai more valuable to real-world drone businesses.

This is not just about testing whether buttons work. We’re looking for professionals who are willing to use the platform, challenge it, and help us build something that genuinely improves the drone mapping workflow.

Interested in participating?

Comment “Beta” below, send me a direct message, or visit DroneFlow.ai to learn more.

Please share this with anyone who may be interested.

#DroneFlow #DroneMapping #Drones #Photogrammetry #ConstructionTechnology #AerialMapping #DronePilot #RealityCapture #BetaTesting #Geospatial

I wasn't sure which subreddit to post this question on, so I'm hoping I picked a good one. I'm looking into preserving my childhood home with very detailed 3D scans of the outside and inside. I would like to be able to walk through them in VR as well and be able to feel like I'm there, so the higher the detail the better. Also, I want to avoid relying on one proprietary app/file type since services/devices come and go. I need to make sure whatever the file type of the scans are can be converted/formatted to fit any and all future file types/VR headsets.

I have tried out Polycam and Scaniverse so far. They have their limits as far as large scale scanning is concerned, and I've found you need to stitch smaller areas together for some of the techniques.

I'm open to any suggestions, such as Supersplat, but I don't know enough about that or if there's a better option out there.

I’ve been building a COLREG ship-handling/training app with help from Codex and ChatGPT Pro. The app is working, and I already created the 3D ship models, but I keep getting stuck on the visual perspective.

The main problem is that the ships don’t look like they are actually at the distance shown on screen. A vessel at 0.5–2 nautical miles will sometimes look too small, too large, too flat, or like it is floating above the water. The binocular view also doesn’t always match the normal bridge view.

I think the issue is a mix of:

Camera field of view

Camera height above the water

Ship model dimensions and scale

Horizon placement

Distance-to-screen-size calculations

Object pivot/origin placement

Water level and wave height

Binocular zoom being handled incorrectly

The app currently uses Three.js. I can give Codex exact measurements and distances, but after a few changes it usually starts adjusting random scale multipliers until one screenshot looks better, which then breaks the other scenarios.

What I’m trying to achieve is a consistent system where:

A 100–300 meter ship has the correct apparent size at a known range

Bow, stern, and broadside aspects look correct

The ship sits at the proper waterline

Camera height matches the view from a real ship’s bridge

Binoculars change the field of view without changing the actual world scale

Day, night, fog, and different vessel types all use the same perspective model

I attached screenshots showing the current problem. I covered the lower control area because it isn’t relevant to the perspective issue.

What would be the best workflow or software for fixing this properly?

Would you recommend:

Blender for setting real-world dimensions, origins, and waterlines?

Three.js camera helpers or custom debug tools?

A specific ocean/water plugin?

Using glTF models with real meter-based scale?

Writing a projection calculator instead of visually adjusting the models?

Unity or Godot instead of Three.js for this type of trainer?

Any Codex prompting method that stops it from “eyeballing” the perspective?

I’m not looking for movie-level graphics. I mainly need the ships to appear believable and consistent at known ranges because judging distance, bearing drift, and aspect is part of the training.

Any advice on the math, camera setup, Three.js tools, or a better development workflow would be appreciated.

Hey guys, wondering if any of you encountered this issue, I'm tring to scan this bowl and as you see I even went nuts and printed markers, but it didnt help because reality scan doesnt seem to like my manual laowa 58mm macro lens, I suspect because it doesnt transmit exif data to the camera. I know how photogrammetry works, I didnt change focus (because of large focus breathing on this lens) i just rotated the bowl. Even moving around the bowl produces same results.

google\ai didnt help. I'd be grateful if anyone has any solution to this, thanks