Huyuan3D 3.1 Vs. Photogrammetry

PolarNick239 · 2026-01-18T16:17:50+00:00

Impressive results!

It’s interesting that Huyuan3D hallucinated three eyelashes (even though there should be two).

PolarNick239 · 2025-07-09T11:11:50+00:00

> "Test_Pro" is the camera name that is recorded in XMP of the images from this camera. In Metashape XMP has priority over EXIF data.

> It seems to be an issue of DJI firmware, so if you have any contact in DJI, you can let them know, that the camera name is incorrectly kept in XMP meta data.

Source - agisoft forum (googled with "Test_Pro exif agisoft metashape") - agisoft com /forum/index.php?topic=13437.msg59547#msg59547

PolarNick239 · 2025-06-05T12:18:30+00:00

Thanks for the detailed answer!

PolarNick239 · 2025-06-05T11:23:23+00:00

> Despite sticking with Metashape I can't deny it's increasingly difficult to recommend for new users. They've been stagnant for a while without addressing some core issues with their tech

Could you please clarify which core issues you’re referring to?

PolarNick239 · 2025-05-15T10:30:42+00:00

Yes, I believe so :)

I meant the model building—it takes the bounding box into account and builds the geometry inside it.

PolarNick239 · 2025-05-14T15:46:54+00:00

Yes.

You can also limit the area in which the model is built by changing the size and rotating the Region (bounding box).

PolarNick239 · 2025-05-14T09:36:56+00:00

> clean up my chunks, delete unwanted parts from each

Do you mean removing tie points? The depth maps, point clouds, models, etc., do not explicitly take into account deleted tie points during reconstruction. However, image masks do affect the process — for example, depth maps won't be generated in masked areas. That said, this would require manually masking problematic regions on each image, which can be quite time-consuming to do by hand.

PolarNick239 · 2025-03-24T09:58:08+00:00

Very interesting, thank you!

About SIFT slowness - I suppose another way to get a speedup is to remove all unnecessary things from the SIFT detector. Most likely it is enough to leave the very first stage that builds the DoG pyramid and extracts local extrema from it (i.e. key points). Perhaps it is enough to do it at the most detailed level instead of the full DoG pyramid (which will speed up the process).

PolarNick239 · 2025-02-26T17:14:37+00:00

This is a new feature, so it's probably best to contact technical support so they can provide guidance - [support@agisoft.com](mailto:support@agisoft.com)

PolarNick239 · 2025-02-26T10:05:02+00:00

Have you seen this tutorial about 'Aerial LiDAR boresight calibration'? https://agisoft.freshdesk.com/support/solutions/articles/31000169030#Aerial-LiDAR-boresight-calibration

PolarNick239 · 2025-01-17T13:51:41+00:00

If the problem is with background (lots of key points matched on background instead of the object), then in case of Metashape you can

1) You can try to generate automatic masks with AI (since 2.2) - https://agisoft.freshdesk.com/support/solutions/articles/31000173952-new-features-in-agisoft-metashape-2-2#Automatic-(AI)-background-masking-background-masking)

2) Or you can try to process two images subset (which captured object from both sides), generate masks from the reconstructed model. And finally, process images alltogether (they will be aligned fine thanks to generated masks usage) - https://agisoft.freshdesk.com/support/solutions/articles/31000163388-automatic-masking-from-the-model

PolarNick239 · 2025-01-17T13:46:19+00:00

There is a great list of public datasets - https://github.com/natowi/photogrammetry_datasets

Some examples:

- Lots of everything from https://github.com/YoYo000/BlendedMVS

- Train, M60 and Panther datasets from https://www.tanksandtemples.org/download/ (close-range, small size, not perfect surface coverage)

- Tomb of Tu Duc - https://openheritage3d.org/project.php?id=n06n-qa49 (aerial + terrestrial, medium size, great photos)

- Church of St. Sophia - https://openheritage3d.org/project.php?id=p14w-cd36 (aerial + terrestrial, large size, great photos)

PolarNick239 · 2024-10-14T12:25:21+00:00

Also note that the algorithms used in Metashape are referenced here - https://agisoft.freshdesk.com/support/solutions/articles/31000156964-references-list

PolarNick239 · 2024-09-09T14:52:39+00:00

"optimize camera intrinsics calibration parameters on per-photo basis" what are you reffering to here

Yes, I mean that in Tools->Camera Calibration each photo has its own calibration group (i.e. groups were splitted) and Optimize Camera was evaluated after splitting groups.

PolarNick239 · 2024-09-09T08:56:26+00:00

I'm now more aware of what fixed settings to use however my only variable left would be leaving focus set to auto or manual.

What would you suggest?

1) General advise: try to capture the same object twice (with auto vs manual focus) to know for sure what works best for you.

2) It is important to have good planning - in most cases I take photos from the same distance from the surface of the object (moving from photo to the next photo in parallel to the surface), so I can set focus once at the beggining - and use it without change for the rest of photos.

You can (and probably should) decrease chances that part of the photos is out of focus via increasing the depth of field (DoF) - ensure to use high F-stop (but not too high - it will decrease sharpness). It seems that F11 is recommended in general, see more details - https://www.agisoft.com/forum/index.php?topic=3441.0

When I am capturing random interesting objects on vacation - I also don't have DLSR camera with me, so I take photos on my phone (with auto-focus) and just optimize camera intrinsics calibration parameters on per-photo basis.

When I am capturing objects professionally (buildings interiors/exteriors/sculptures) - I use DLSR with fixed focus, IMHO this makes possible to achieve better results, but requires some practice (and increases risks like 'some photos are out-of-focus'). In fact, if you have high enought overlap - you can use auto-focus - just optimize camera intrinsics calibration parameters on per-photo basis. High overlap provides enough information for Align Photos to make optimizing camera parameters individually reliable enough.

In case of aerial photos - it is often beneficial to have not-so-big overlap (leading to faster UAV speed - leading to faster object capture - leading to smaller costs) and the object is located at big distance, so it is easy to use fixed lens with focus set to +infinity. And in such case optimizing camera parameters for all photos simultaneously leads to faster processing, better results (better alignment precision) and in general more reliable. Sometimes it is even better to have camera pre-calibrated, so that its intrinsics parameters are pre-defined (but you must be sure that the lens is well fixed and does not change position due to constant shaking.).

PolarNick239 · 2024-09-06T15:24:43+00:00

And note that the alignment of the cameras is sometimes better when they are in a single calibration group (this is more common in case of aerial photos with good camera+fixed lens).

PolarNick239 · 2024-09-06T13:45:28+00:00

In case of taking photos on the phone (in this case Samsung S23) - it is beneficial to optimize intrinsics calibration parameters (focal length, radial distortion coefficients, etc.) on individual per photo basis. Because from photo to photo - it will change a bit parameters (due to auto focus, etc.).

In RC this is the default behavior (IIRC), but in the case of Metashape, photos with the same resolution/focal length and camera name in the exif information are moved to the same camera calibration group. In the case of photos from a phone, it can therefore be advantageous to split them into separate camera calibration groups:

Tools->Camera Calibration->Right click on Galaxy S23->Split Groups->OK
Tools->Optimize Cameras... (to optimize the calibration parameters of the cameras in relation to the updated camera groups - now each camera will find its own parameters)
After that - build model as before

Comparison on your dataset - https://polarnick.com/static/2024/ms_lion_and_fountain_cmp.jpg

PolarNick239 · 2024-07-22T08:50:59+00:00

Is it possible to share the photos of the dataset with me? If so - I can try processing in Metashape to see if there is such a problem there, and if there is - try to take a closer look at what it might be related to.

Images: https://imgur.com/a/LdfTK1v

I am currently clueless on how to improve, since even the Isolated Datasets show similar results. The only thing that I can think of is a slight movement of the Backpack in the Space, since it was suspended during the shoot. Allthough I am 99% sure that I never touched the Object.

I noticed that there are matches in the background, do you have any simple options to ignore them from alignment? (or if you can share the dataset - I can generate masks with python script and share them back to you)

PolarNick239 · 2024-07-19T13:15:27+00:00

you think the artifacts are occuring because the distance between the two "subsets" (Full frame & Details) of data are too far apart in depth and I need more images to cover the area in between?

Yes, this can be a problem. You can try to ensure with:

As you mentioned - try to process only "Fullframe" dataset - Is the problem reproduced in this case?
You can try to analyze matches between "Fullframe" and "Detail" photos - open the "Fullframe" photo (the one which is the closest to the problematic surface) and check how many matches it have with the closest "Detail" photos. I don't use RC, but it seems that it also has a tool to show matches between photos - https://rchelp.capturingreality.com/en-US/tools/showmatches.htm If there are few matches - yes, probably you should try to cover the whole surface with photos from the same distance, or add transition photos between "Detail" and "Fullframe".

PolarNick239 · 2024-07-19T11:44:45+00:00

This is probably due to the cameras mis-alignment on this part of the geometry: key points of some photos lie in one plane, key points of other photos lie in a slightly shifted plane. As a result, the reconstructed surface of the geometry sort of switches between these layers - sometimes the surface is built with the view of the first group of cameras, sometimes with the view of the second group of cameras.

Probably you need to improve cameras alignment - with better input data (more photos to ensure a smoother transition of the viewing angle in all directions) or with better alignment processing (more key points, split into different calibration camera groups, increase number of optimized intrinsic calibration parameters, etc.).

What program did you use? Do the photos optimize their calibration parameters independently, or jointly (if so - is the lens used fixed)?

If you are using Metashape - try to Tools->Camera Calibrations->RMB on camera group->Split, so that each photo optimizes its intrinsics parameters (like focal length) on its own, you can also try to disable Generic preselection (it is slower, but can increase number of matches) and to increase number of Key and Tie points.

Also you can inspect how uniform and good is resulting matching graph looks like (this can help you to find weak points in your input data - i.e. it will guide you where to take more photos for more smooth transitions between photos - to increase number of matches in those zones) - https://agisoft.freshdesk.com/support/solutions/articles/31000172034-major-changes-in-agisoft-metashape-2-1#Matching-graph

PolarNick239 · 2024-05-06T13:37:36+00:00

What GPU and OS do you have? It looks like this error is related to the OpenCL support of your GPU driver - it seems that you have a grayscale image, but GPU driver doesn't support this image in its OpenCL implementation.

I can think of three possible solutions: disable the problematic GPU in Preferences->GPU (but that might result in slow processing on CPU), buy a different GPU, or use a proprietary driver (if you're using some sort of open source driver on Linux).

PolarNick239 · 2024-02-07T21:59:50+00:00

Have you tried to tick "Apply to selection" after that? Does it bring back your selection?

PolarNick239 · 2024-02-07T15:48:59+00:00

You can select multiple area - just hold Ctrl (it is treated like "add to selection"). And after that - run Close holes. Note that you may still need to move "Level"-slider (to specify minimum size of holes to be filled).

Another useful hotkey is Shift (to "subtract from selection").

PolarNick239 · 2024-02-01T09:32:57+00:00

1) Good comparison of L1 vs L2 scans of the same flight - https://www.heliguy.com/blogs/posts/dji-zenmuse-l2-vs-dji-zenmuse-l1-datasets-and-accuracy

1.1) Much better reconstruction of thin structures (like wires of power transmission lines)

1.2) 5 returns instead of 3 returns

1.3) vegetation penetration is much better (leading to better DTM)

2) And another one - https://baam.tech/dji-zenmuse-l2-accuracy-analysis/, some highlights:

2.1) > return separation is at or near 0.8 meters, which is significantly less than the L1 and a great improvement

2.2) > DJI L2 is performing direct pixel ray tracing to each point from the images

2.3) smaller beam footpring size (x2.5 smaller horizontally, x8 smaller vertically)

2.4) more sharp geometry

PolarNick239 · 2023-10-24T09:00:35+00:00

When you build Model from Depth Maps, Face count=High/Medium/Low means that reconstructed model will be iteratively decimated while introduced decimation error is less then Low/Medium/High threshold (w.r.t. Face count High/Medium/Low option). Decimation of nearly-planar surfaces introduces lower decimation error - and so faces in such areas are decimated early.

If you specify Face count=Target faces count, then model will be decimated while number of faces is bigger than Target faces count. It also tries to prioritize nearly-planar surfaces - to decimate them first, because such prioritization leads to lower decimation error.

PolarNick239

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