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Messages - Jobbo90

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Camera Calibration / Re: Difference in camera calibration values
« on: November 04, 2020, 01:28:48 PM »
Hi all,

I haven't been able to resolve the doming effect that is visible in the previous post. If anybody want to give it a go at the data sample I used, that would be much appreciated:

I'm curious what others are finding and might have some valuable suggestions to improve for future efforts.


Camera Calibration / Re: Camera calibration distorts reconstructed model
« on: November 04, 2020, 11:10:45 AM »
Hi WinkelSchraube,

You had any progress on this? i'm experiencing similar issues with doming/distortion of the models I have.

Camera Calibration / Re: Difference in camera calibration values
« on: October 30, 2020, 04:54:12 PM »
Dear Paolo,

Thank you for your swift reply, I indeed followed these steps before but now I also tried with the newest agisoft version. It does indeed improve the Cx, Cy and f estimates. Yet I'm still left with a doming effect in my model (sparse cloud) as can be seen from the pictures. Do think this can be improved before or during alignment or is that really only possible with the ground control points? I have them for this project but due to restricted distribution through out the scene I'd like to get the sparse point cloud as good as it gets before I update cameras after including markers.

It seems to me that especially b1 and b2 are causing some problems, I also tried the workflow you suggested in the post by fixing b1,b2 together with f, cx and cy before aligntment. And then during the update cameras steps I include all of those camera parameters, not sure of that makes a difference?

I often follow this workflow: where the markers are introduced before filtering on the reprojection error..


Camera Calibration / Difference in camera calibration values
« on: October 29, 2020, 02:37:29 PM »
Hi All,

Following up on some discussions I found here I started diving into two projects I have over the same area but with 9 months difference between acquisition. Both projects were acquired with the same DJI mavic pro 2 and the same flight paths planned in Lithci (nadir and off-nadir pictures at each location). The only difference is the flight height which was 50 meters in 2019 and 75 meters in 2020 (relative windy conditions, so the ensure sufficient overlap I dicided to increase the flying height). The results showed here contain roughly 50% of the pictures processed inAgisoft metashape 1.5.2 (I'll try 1.6.x soon as well!). All alignments are at 'standard' settings:
high accuracy, generic preselection ON, reference preselection ON, key point limit 40.000, tie point limit 4.000, adapative camera model fitting ON.

I started notcing I considerable doming effect in the 2020 data and found in other discussions here that this might be related to the automatic camera calibration performed by Agisoft. When I look at the estimated calibration camera's I get confused.  So for 2019 all seems to be fine with estimated f at 4289.5 (intial at 4256), and cx,cy at 20 and -49 respectively and b1 and b2 at 0.23 and 0.38. Allthough I wonder why the estimated f is different and if so if the difference is acceptable. (hopefully the example images are clear!)
For 2020 is becomes messy: estimated f is at 3366.8, cx and cy at 63.8 and 1556.1 respectively, and b1 and b2 are -66 and 22.  Remember, these are the same drone and flight lines (except the heigt). So to check if something else changed I looked at another fleight campaign I did around the same date in 2020. Here the camera calibration values are way better: estimated f at 4256 (it is the same as the intial...) and cx and cy at 17 and -27 respectively. b1 abd b2 are at -22 and -0.15, so only b1 is still way off.

So my questions is where these difference come from, is it camera settings during flying or something else? And more specifically what can I do to improve the alignment of the 2020 data?
I already read something about enabling rolling shutter before alignment (this doesn't improve the 2020 dataset) :

Also trying to estimate the camera calibration parameters beforehand and 'fixing' them seems to be an option:
Just wondering if that is going to help?

Some additional info:
both datasets are part of a larger set that also contains 20 - 25 GCP markers (dGPS quality) that do help for the overal quality of the outputs. Yet especially the DEM suffers from significant doming in the 2020 dataset, but also the orthophoto is distorted to the large noise, even after gradual selecting tie points with relatively strict reprojection error values, reconstration uncertainty and projection accuracy filters. Also the lay-out of the markers is not optimal due to limited accessibility at the boundaries (muddy intertidal area). This is why i'd like to see if there is improvements possible in the allignment and camera callibration.

Curious to see if there is somebody who can direct me towards a potential solution for the 2020 dataset!

General / Re: Alignment Workflow - Issues
« on: August 12, 2019, 10:19:50 AM »
Have you made any progress, be interested to hear if you came up with a solution? I'm experiencing similar issues.


General / Re: Problem with Align in Methashape DJI Pro 4
« on: August 08, 2019, 11:56:30 PM »
If the whole scene is full of trees you are going to have a problem, especially when you took the images on a windy day.

General / Re: Phantom 4 RTK vertical system settings case
« on: August 08, 2019, 03:47:57 PM »
Hi Kereplaz,

Did you add GCP's to the project and apply that during your alignment steps? And did you apply optimize camera steps along the way?

Try the following procedures and see if it helps:


General / Re: Problem with Align in Methashape DJI Pro 4
« on: August 08, 2019, 03:31:33 PM »
Assuming that overlap is not the issue you could try the following:

After initial allignment where some of your photo's don't allign, run allignment a second time. Make sure you uncheck 'reset current alignment', in this way you keep the initially aligned pictures.

Another option would be to reduce the tie point limit to 0 (no limit) and increase key point limit to perhaps 80.000

not sure what kind of surface you are trying to map but when there is vegetation (especially trees) you could consider creating image masks for those areas that are generally difficult to align (water/trees)

I'm running into similar problems so let me know what works!

General / Mis-alignmened pictures in UAV project
« on: August 08, 2019, 03:15:01 PM »
Hi All,

I have UAV(Mavic Pro2) project where a part of my pictures don't align or align in some sort of sphere shape. Because it is only part of my picterues that don't align I'm confident that the overlap in my flight plan is sufficient. The pictures not aligning contain, beach forest and water so i'm guessing part of the problem is relatd to the surface cover types. I'm just curious if it is possible to play around with the Agisoft settings to force metashape into alignment. In the near future i'd like to return to the location and where possible optimize the flight plan to ensure correct alignment

Project details:
- .dng pictures (relative 'dark' if you ask me?), a subset of 280 pictures of which 120 are not aligning propperly. The original dataset contains over 1100 pictuers wich are aligning just fine, just this spatial subset is not working.
- On each location a nadir and off-nadir picture. Locatoins are spaced 40 meters apart and flown at 100m, despite the exif metadata suggestin 65 meters.

What kind of steps would you recommend to try? I played around with different levels of accuracy, watermasks and all the other alignment options. Also I'm wondering if I could do some image processing or if it helps to explose the camera calibration possibilities?

Enclosed there is a picture of the resulting alignment test and a sample picture.

Tahnks for your comments!

General / Re: Deformed scan
« on: April 26, 2018, 01:44:29 PM »
Are you using GCP points? If so, might be worth to check if the coordinates you use are correct, else it forces the model in an incorrect projection causing deformations in the final object..

I’m not using markers or GCP points. My workflow is to import the images; disable the ones that are not great; apply mask if necessary; align photos; build dense cloud, mesh and texture.

In the case of this model it wasn’t necessary to apply a mask since the only background information in the photos was mostly the scale beneath the object.

Then it might be worth to implement a arbitrarty reference system with some control points? Use the squares of the underlying surface with elevation of 0 and a x and y coordinate from an arbitrary chosen 0,0? See if it helps to force the model in correct dimensions. Else you might have to describe in some more details your workflow in order to be able to answer this question..

General / Re: Deformed scan
« on: April 25, 2018, 02:12:44 PM »
Are you using GCP points? If so, might be worth to check if the coordinates you use are correct, else it forces the model in an incorrect projection causing deformations in the final object..

General / Re: Missing photos
« on: April 25, 2018, 02:08:24 PM »
in my experience photo's don't align properly when you don't have sufficient tie points. This can result from insufficient overlap or pictures with homogeneous areas (e.g. water). You can try to run the alignment with higher settings or use control points, probably what you refer to as the manual marker set up? Check the manuals for a starting point perhaps?

Good luck!

General / Import Camera Locations from xml file
« on: April 24, 2018, 04:52:37 PM »
Hi All,

I have a .xml file containing all video and photo locations (see attachment) from a drone that's been used for mapping and video surveillance.

What is the best way for me to acquire the relevant photo locations for aligning and building a digital elevation model?

The new flight is stored like this:
<ori yaw="+179.40" pitch="-090.00" roll="-004.41    " />
   <time value="2018:04:20 14:43:35"/>
   <note par="Flight" value="0001    " />
   <time value="2018:04:20 14:47:07"/>
   <note par="Flight" value="0001    " />
   <time value="2018:04:20 14:47:52"/>
   <gps lat="+05.90003016" lng="-055.22443772" alt="-0069.752" />

following by all the information from pictures in the following format:
<ori yaw="+124.65" pitch="-090.00" roll="-003.80    " />
<image path="D:\DCIM\100MEDIA\AMBA1124.jpg">
   <time value="2018:04:20 14:47:55"/>
   <gps lat="+05.90003016" lng="-055.22443772" alt="-0069.752" />

As far as I can tell the images are not stored with an EXIF like format containing the locations but rather have an separate file containing the locations. I also tried to change the relative pathnames with the locations of the pictures. Do I need to rewrite the file in the described .csv format in order to attach the locations to the pictures (tideous work :-X)?


General / Dense cloud or Sparse cloud?
« on: January 17, 2017, 10:06:10 AM »
Hi All,

I'm experimenting around with Agisoft and have a bit of a theoretical question.

I'm working with a close range photogrammetry project (300-400 pics), taking pictures of geomorphology of an area compromosing 2 by 8 meters. I used a Canon EOS 1300D camera with a focal length around 40mm resulting in high density point clouds.
Now i'm wondering if it is worth to bypass the build-dense cloud step for computation speed and data reduction purposes, this will result in building a mesh based on the sparse cloud.

- How would this affect the accuracy of the final DEM (I'm trying to reach mm accuracy)?
- What is it exactly that the dense cloud step does?
- If I for example run algnment at high settings I already end up with 9.5 million points on such a small area, anybody can tell if this is sufficient?

At some stage i'd like to test how the bypassing of the dense cloud affects the accuracy, but at the moment I don't have sufficient time and resources available so im wondering if other already considered these steps.

Curious about your thoughts!

General / Re: Orientation in model with arbitrary coordinate system
« on: January 10, 2017, 11:56:10 AM »
Thank you so much, I wasn't aware that in an arbitrary coordinate system swapping X and Y coordinates makes the difference. Together with pressing update on the right time in the workflow solved it for me, thank you!

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