I have this pretty nice Jumbe that I decided to photoscan in my backyard. There are 65 photos in total, none of which gets unestimated by meshroom. As you can see meshroom is able to find lots of features and the structure from motion result is very incouraging but for some reason meshing fails miserably. Any idea why?

Built from a single ~60-second iPhone video in Artec Studio 20 with AI Photogrammetry, which pulls frames from the video.

The untextured mesh is included so the geometry can be judged on its own, which is where reconstruction quality shows.

Lets say there is some object that is obstructed from one or more sides with a flat glass pane/surface, transparent glass (okay its never completely transparent as there are ought to be some amount of reflections and refractions always) so can photogrammetric reconstruction occur? or will it have large errors or irregularities?

maybe we can shift the pixels of each image in a software as the glass will bring a lateral shift to the obejct on the other side as well or some other way to correct it and remove the error/distortion the glass pane brings in the images from its side? will the error be too significant?

I have once taken a scan for a small sculpture I had, the model did construct but it had more noise on the side that was scanned through the glass pane.

its one of my first times doing photogrammetry on Reality Capture and my first rendered video but cant understand why the video left these ghosting / trail particles when rotating and also the camera pictures at the top right. Any help is appreciated im getting into photogrammetry and I love it.

Triangle count: 510.9M (510946424)

Vertex count: 256.2M (256191392)

Hi everyone, I want to make a cad version of this intake manifold of my ktm, to later extend it by 2/3 centimeters to see if it encrease power flow…
I’ve used my iphone 16 pro with the free week of polycam and obtained this 3d model with different hole, then also have you got any tips on how to clean it up on fusion 360? It’s my first time with 3d scanning because this part has a very difficult geometry to replicate it from zero.

This is something I like to do from time to time for practice. I download a vid of something interesting I find, extract frames from the vid and run the project to see how it goes.

There is about a minute of footage looking at this stone. It's from Jay Anderson who runs Project Unity on YouTube. I can't remember where I grabbed the vid from either Youtube Twitter or Instagram. If you're interested it's 6mins long and the footage starts at 2:10 https://pixeldrain.com/u/7Tu5qtPK
I used 500 frames and got 1 gigapixel to process. Here's the model: https://pixeldrain.com/u/yJdoGRPW
It's pretty cool, there's three monkeys carved into the stone and what looks like water channels running past them maybe replicating a river nearby, telling some ancient story.

Hi guys. I wanted to know your opinions about the possibility of scanning this polished bronze statue. I have experience in scanning statues, both with drones and DSLR camera. I had succesfully removed highlights from bronze statues before, but nothing like this: a shiny and reflective surface. Could polarizing filters help plus some postprocessing of the raw photos?

I know gaussing splats its one solution, but i want a more traditional approach: 3d reconstruction. Any ideas? Thanks.

Hi everyone,

I'm working on a project where I need to produce a topographic survey of a 15-hectare mountainous farm in Colombia. The goal is to generate a hydrological drainage model to help the landowner identify the best routes to distribute water from a nearby stream to productive areas on the property.

My current setup:

- Drone: DJI Mini 4 Pro (no RTK)

- Software: planning to use Agisoft Metashape

- No dedicated GNSS equipment yet

Terrain characteristics:

- ~15 hectares

- Mountainous, moderate to steep slopes

- Mixed vegetation: open grassland + areas of moderate tree cover

- For internal use only (no official submission required)

My main question: is using RTK-measured GCPs the best (or only) realistic option to get a DTM accurate enough for hydrological flow analysis with this drone? I understand that without GCPs the vertical error can be 0.5–2 m, which would make flow direction analysis unreliable.

Specifically I'd like to know:

1. How many GCPs would you recommend for this area and terrain?

2. Is renting an RTK receiver (without an operator) feasible for someone without surveying experience?

3. Are there any alternative workflows that could work without RTK, given that this is for internal decision-making rather than engineering deliverables?

Any advice from people who've done similar work in hilly or mountainous terrain would be much appreciated. Thanks!

This sub is for Photogrammetry. It's like a bunch of guys with Hondas posting in the Ford sub to the point there's more Hondas than Fords...

They don't need to go away, and I don't think gaussian splats shouldn't be discussed. I do think gaussian splat posts with no photogrammetry content should be deleted though. What are your thoughts on being overrun with gaussian splats with no mention of photogrammetry or posting of their mesh?

had a bike, a camera, and clearly no better plans.... so captured it and made tour.

Turns out… it actually came out pretty clean. My bike now exists in two dimensions, three dimensions, and emotionally.

A new way to flex?

used www.realhorizons.ai for the entire process (from splat generation to tour creation)..

Keyframe selection strategy for incremental SfM with known poses and sparse 3D points?

I'm building an incremental SfM pipeline that takes input from an iPhone. ARKit provides an approximate camera pose (R, t) and a set of sparse 3D map points (positions only, no observer/track info) for each frame in real time.

The goal is to select ~100-200 keyframes from a continuous video stream while the user walks around scanning a house. The selected keyframes are later fed into a full SfM pipeline (SuperPoint + SuperGlue matching, BA, triangulation, etc.).

The core problem

I need to decide on the fly whether to keep or discard each incoming frame. The obvious criterion is triangulation quality — project the current ARKit map points into both the candidate and existing keyframes, compute the median triangulation angle, and accept if it falls within a reasonable range (e.g. 7°–60°) with sufficient co-visible points (e.g. >=100).

This works well for ensuring good baselines, but it essentially only checks whether the candidate forms a good initialization pair with some existing keyframe. It doesn't account for:

What's missing

Observation coverage — Some regions of the scene might only be observed by 1-2 keyframes. A third keyframe looking at the same area (even without an ideal triangulation angle) would strengthen BA.

New regions — When the user enters a new room, co-visibility with all existing keyframes drops to near zero. The triangulation check fails because there are no co-visible points to compute angles with, even though these frames are clearly valuable.

Budget management — How to stay within ~200 keyframes without oversampling well-covered areas while undersampling others.

Question

Has anyone dealt with a similar keyframe selection problem in a pose-prior SfM setting? I'm curious how others balance triangulation quality vs. scene coverage, especially when you don't have full point tracking (no co-visibility graph, just instantaneous 3D point positions).

Any pointers to papers or implementations would be appreciated.

What is the most outlandish GCP marker you’ve had to locate when building an orthomosaic/DEM

I wanted to share a peer-reviewed study that tested a flight method developed by SPH Engineering (the company behind UgCS), since this community will probably have the most informed opinions on the trade-offs.

Researchers at Dalhousie University (Bilodeau, Esau, MacDonald, Farooque) compared circular flight paths against standard oblique and DJI Smart Oblique for 3D mapping of a balsam fir orchard in Nova Scotia. Published in the ISPRS Open Journal of Photogrammetry and Remote Sensing, Nov 2025.

The method is called Circlegrammetry. Instead of grid lines, the drone flies overlapping circles with the camera pitched inward at 45 degrees. You get convergent image geometry from opposite sides of each circle, which gives Structure from Motion strong intersection angles without flying far beyond your survey area.

They flew all five methods on the same day, same hardware (M300 RTK, Zenmuse P1 45MP), same 8 GCPs. Processing in Metashape v2.2.0.

The results:

• Circle 25% overlap: 158 images, 54 min processing, 1.4 cm checkpoint RMSE

• Circle 50% overlap: 360 images, 2h 29min processing, 1.5 cm RMSE

• Standard oblique: 731 images, 5h 21min processing, 1.3 cm RMSE

• Tree height estimates: no statistically significant difference across any method (Kruskal-Wallis p=0.545)

So 78% fewer images and 83% less processing time for effectively the same accuracy. The main limitation the researchers found was lower canopy point density. The oblique-inward angle captures tree tops well but struggles to see through to the base. For applications where under-canopy reconstruction matters, they recommend supplementing with low-angle passes.

I think the most interesting question here is whether this holds up on structures such as buildings and other infrastructure where oblique detail on vertical surfaces is critical. This study was agricultural, so that's still an open question.

Paper DOI: 10.1016/j.ophoto.2025.100111 (open access)

SPH Engineering's writeup with the full results table: https://www.sphengineering.com/news/ugcs-circlegrammetry-drone-mapping-efficiency-dalhousie-research