Topics

1

General / Texture suddenly messed up, why?

« on: Today at 09:36:53 AM »

Hi forum!

Suddenly i got projects where the texture gets messed up and i cant figure out why. The shaded model looks like it should but something happens with the texture. Here is 2 screenshots with the shaded model and the textured one. Anyone knows why this happens?

2

General / HELP!Almost loosing it, sparse cloud at -512m elevation after alignment with RTK

« on: March 29, 2024, 10:41:25 AM »

Hello forum, hoping someone can help me out here.
I have a flight with our Matrice300RTK with P1 camera and 35mm lens. When i look at the pictures the look the same as all other hundreds flights we have done so everything looks great. RTK data looks fine, flight height also looks great comparint to our gcps. BUT, after aligning phothos the result is really poor. Aligning eith highest will give med just some photos aligned and using loweat will aling all of them but i always get the sparse cloud at minus 500m. It does not matter what i do, what am i missing here?? This looks like it should work like its an ordinary day at work but now i have tried fixing this for a lot of hours without any succes, Posting ascreenshot also with results and settings.

Have a nice day

3

General / Need explanation,Error in pics and gcp depending of what i choose for optimizing

« on: September 20, 2023, 03:19:07 PM »

Hi forum!

I have a big problem, its because i dont really understand whats happpening så it would be great if someone can axplain it to me. We are using a matrice 300 with P1 camera and when computing projects we get different results. We always use gcp:s so we know that the data should be correct but we get wrong data.

First i align the photos and place the gcps:s. When i do "optimize" i do this with all pics unchecked and just gcp:s checked. This usually gives me an error of 0.005 or soemthing, really good. But if i look at the errors of the pics they could get errors up to 1m and when checking the cloud to the gcp.s we get big differences and really poor accuracy. What does really happen when i uncheck all pics and just have the gcp:s checked? Why do i get that  big errors on the pics?

If i align and then do the optimize with both pics and gcp:s checked i get more bad accuracy on the gcp:s but much better error on the pics. AND the cloud is getting much more correct. Is this the way to compute, with both gcp:s and pics checked when optimizing?

I must also say that i had experiences when using both pics and gcp:s checked and had really bad result becasuse of big error on pics and when unchecking them helped. Can someone please explain to me how this works and what workflow that should be used, i cant understand it.

I will attach 2 screenshots here also with optimized checked and not checked.

Thanks!

4

General / Photos messing up alignment, how to fix?

« on: April 06, 2023, 04:24:01 PM »

Hi all.

i have got a big problem here. We are using a Matrice300RTK drone and get really nice photos. The last 6  projects we noticed that we got warnings about the writing to the SD-card but nothing more. When importing the photos in Agisoft and doing aling somthing really strange happens. The spars cloud gets really messy i all ways and when importing my gcps its not even near and i cant fix it. Even that we got RTK-data in the pics it dont help. Any ideas how i can fix this, is there any way i can use the photos without the RTK data and how do i do that? Will add a screenshot here in the post.

5

General / HELP! suddenly camera errors, has something changed in settings?

« on: March 29, 2023, 07:06:44 PM »

Hi forum!

This is really strange, i seem to have a big issue with camera errors. I dont really know what all numbers means but if i compare them to earlier projects from late 2022 its a big difference. Today i saw that i had quit big differences in height when comparing to earlier flights, 25cm as the biggest. I am using a DJI Matrice 300RTK with a P1 camera so i get really good pictures and the gcp:s are measured with trimble R12i gps with really good accuracy and still i get a really poor result. When looking at older flights with the same drone i have perfect result and the estimated error in the reference cameras is as highest 10cm for only 1 pic and then its from 3cm down to millimeters for the rest. Looking at my latest flights its now from 2m error for the worst pics and 70cm for the best! After the alignment of the pictures i import the GCP:s and the are not spot on my ground markings, the are 5-10cm of and they used to be spot in since the RTK on the drone always is good. What am i missing here? I really hope someone can help med because this is a BIG problem for me right now. I will attach 2 screendumps also, one with a project wich is good from december 2022 and one from a flight a couple of days ago so you can see the difference.

Have a good day!

6

General / Mesh gets strange, what am i doing wrong?

« on: December 05, 2022, 08:35:44 PM »

Hi!

After updating i must have changed something in my settings when creating a mesh but i can figure out what settings. Before my update i did not get many holes even if i had some trees in the cloud and the edges of the mesh was not like it is now, like a wall. I have tried a lot of diffrent settings but i get the same. Anbybody have a idea of what settings to use for creating the mesh to avoid getting holes and a wall at the edge?

7

General / DJI MINI2, distortion in cloud, what am i doing wrong?

« on: March 29, 2022, 08:56:22 PM »

Hi all!

We did another test with using photos from our mavic mini 2 but this time i get this (pic attatched), points everywhere after the alignment. Last time i used photos from the mini it worked quite well but now i must be doing something wrong and i cant understand why. Anybodu knows why?

8

General / Phantom 4 RTK - how to use pictures when flight without RTK?

« on: October 26, 2021, 01:09:29 PM »

Hi!

Probably this is a easy question but i cant figure it out. Sometimes when we fly we loose RTK connection in the air when flying and then it connect again and sometimes we fly a whole flight without RTK. I dont know why this happens but it does.
Anyway, when i try to use this pictures in Agisoft i get a really big error when using my GCP:s. How can i get arround this? It seems like it is something in the metadata wich is telling Agisoft that the accuracy should be like RTK but it isnt. Can anyone give me a workflow of how to do this?

Have a nice day! 

9

General / HELP wanted! (Dropbox link in the text)

« on: February 11, 2020, 11:15:32 PM »

Hi!
In the dropbox link below i have a project wich is from a phantom 4 and the first chunks was no problem but suddenly everything gets FU when i try to  use my gcp. The pointcloud goes twisted and the fault is a couple of hundred meters and i cant understand what i am doing wrong this time. If i have the photos checked and import my gcp they are placed correct but when i stat to place them everything goes bananas. Can anyone download the project and try do get it right and tell what i am doing wrong? This is from a regular phanthom 4 and i also have a P4 RTK and with that one is never any problems at all.

Thanks in advanced.

https://www.dropbox.com/sh/67zxg1fo455jl73/AABxv3FXyVAv6X_Mc4ZY27xUa?dl=0

10

General / Agisoft to Sketchup with KMZ, what defines the origo?

« on: May 11, 2019, 11:10:09 AM »

Hi!
I am using the kmz export to export from metashape to sketchup and when i import the file in sketchup the placement at north is correct but i want to find out what is controling the origo in sketchup?
I have been testing different things but i cant figure this out. I want to know because if i know what controls this i also can import other stuff to skechup that gets a correct placement. See the pictures

11

General / DJI P4 RTK Workflow

« on: March 02, 2019, 10:59:08 PM »

Hi guys!

I just bought a dji p4 RTK and have made 2 flights. I dont find so much information about using the photos in Agisoft (maybe i have been searching wrong) so i hope you can help me.
So, what is the best workflow?

This is what i did:

1. import photos
2. Align photos, highest
3. Import gcp and changing the settings for coordinate system. For me in sweden its: Project system, SWEREF99. Camera system, wgs84. gcp system SWEREF99. I had only 1 gcp for testing the RTK drone.
(the gcp does not show up in right place, maybe 4m beside but the photos has accuracy 10-40mm)
4. Place the gcp. The accuracy for the gcp will be like 1m
5. Optimize the pictures with both controlpoints and photos checked.
6. Import the exif data (the accuracy for gcp are under 10mm after that)
7. Build pointcloud and do the rest.

This gave me a really good result when comparing with a flight done with an regular P4 and 10 gcp:s. The result when only using 1 gcp was outstanding.

Back to the question, i relly did not know what i did but it seemed to work  so can anyone tell med how i acually should do? 

Have a nice evening!

12

General / All projects works, one is crashing when genereting pointcloud

« on: December 16, 2018, 08:41:07 PM »

Hi!

I have a huge problem, the last week i have tried everything (i think) when genereating a pointcloud in a sertain project and it crashes every time. I have dine 6 other projects this week and none of them has crashed, but this one does not work. 
Here is the crash report, can anyone see the problem?

ARB_texture_non_power_of_two: Yes
ARB_vertex_buffer_object: Yes
CPU: Intel(R) Core(TM) i7-6700HQ CPU @ 2.60GHz
OpenGLMaxTextureSize: 16384
OpenGLRenderer: ANGLE (NVIDIA Quadro M2000M Direct3D9Ex vs_3_0 ps_3_0)
OpenGLStereo: No
OpenGLVendor: Google Inc.
OpenGLVersion: OpenGL ES 2.0 (ANGLE 2.1.99f075dade7c)
ProductName: PhotoScan Pro
System: Windows 64bit
SystemMemory: 39.9 GB
Throttleable: 1
URL:
Vendor: Agisoft
Version: 1.4.2.6205
XLog: imings: rectify: 0.005 disparity: 0.029 borders: 0.001 filter: 0.004 fill: 0
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[GPU] estimating 167x291x96 disparity using 167x291x8u tiles
[GPU] estimating 135x264x96 disparity using 135x264x8u tiles
timings: rectify: 0.02 disparity: 0.334 borders: 0.003 filter: 0.007 fill: 0
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[GPU] estimating 160x165x96 disparity using 160x165x8u tiles
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[GPU] estimating 228x307x96 disparity using 228x307x8u tiles
[GPU] estimating 143x166x96 disparity using 143x166x8u tiles
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timings: rectify: 0.001 disparity: 0.092 borders: 0.001 filter: 0.001 fill: 0
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[GPU] estimating 165x207x96 disparity using 165x207x8u tiles
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timings: rectify: 0.001 disparity: 0.046 borders: 0.001 filter: 0.001 fill: 0
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[GPU] estimating 213x215x96 disparity using 213x215x8u tiles
timings: rectify: 0.002 disparity: 0.046 borders: 0.003 filter: 0.002 fill: 0
[GPU] estimating 214x278x96 disparity using 214x278x8u tiles
timings: rectify: 0.001 disparity: 0.052 borders: 0.001 filter: 0.001 fill: 0
[GPU] estimating 287x296x96 disparity using 287x296x8u tiles
timings: rectify: 0.004 disparity: 0.033 borders: 0.007 filter: 0.007 fill: 0
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[GPU] estimating 211x227x96 disparity using 211x227x8u tiles
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timings: rectify: 0.02 disparity: 0.351 borders: 0.02 filter: 0.011 fill: 0
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[GPU] estimating 128x275x96 disparity using 128x275x8u tiles
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timings: rectify: 0.001 disparity: 0.051 borders: 0.002 filter: 0.001 fill: 0
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[CPU] estimating 179x221x96 disparity using 179x221x8u tiles
[GPU] estimating 121x275x96 disparity using 121x275x8u tiles
timings: rectify: 0.002 disparity: 0.032 borders: 0.002 filter: 0.002 fill: 0
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timings: rectify: 0.009 disparity: 0.206 borders: 0.005 filter: 0.006 fill: 0
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[GPU] estimating 152x150x96 disparity using 152x150x8u tiles
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[GPU] estimating 265x234x96 disparity using 265x234x8u tiles
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timings: rectify: 0.023 disparity: 0.101 borders: 0.002 filter: 0.002 fill: 0
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[GPU] estimating 210x213x96 disparity using 210x213x8u tiles
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[CPU] estimating 134x217x96 disparity using 134x217x8u tiles
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[GPU] estimating 147x273x96 disparity using 147x273x8u tiles
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[GPU] estimating 265x226x96 disparity using 265x226x8u tiles
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[GPU] estimating 181x276x96 disparity using 181x276x8u tiles
timings: rectify: 0.001 disparity: 0.038 borders: 0.003 filter: 0.002 fill: 0
[GPU] estimating 202x299x96 disparity using 202x299x8u tiles
timings: rectify: 0.002 disparity: 0.188 borders: 0.002 filter: 0.003 fill: 0
[GPU] estimating 219x231x96 disparity using 219x231x8u tiles
[CPU] estimating 195x275x96 disparity using 195x275x8u tiles
timings: rectify: 0.012 disparity: 0.039 borders: 0.002 filter: 0.003 fill: 0
[GPU] estimating 117x155x96 disparity using 117x155x8u tiles
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[GPU] estimating 191x286x96 disparity using 191x286x8u tiles
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[GPU] estimating 120x169x96 disparity using 120x169x8u tiles
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[GPU] estimating 185x237x96 disparity using 185x237x8u tiles
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[CPU] estimating 162x286x96 disparity using 162x286x8u tiles
[GPU] estimating 139x253x96 disparity using 139x253x8u tiles
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[GPU] estimating 273x225x96 disparity using 273x225x8u tiles
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[GPU] estimating 147x273x96 disparity using 147x273x8u tiles
timings: rectify: 0.001 disparity: 0.026 borders: 0.001 filter: 0.004 fill: 0
[GPU] estimating 190x286x96 disparity using 190x286x8u tiles
timings: rectify: 0.002 disparity: 0.088 borders: 0.002 filter: 0.001 fill: 0
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[GPU] estimating 238x241x96 disparity using 238x241x8u tiles
timings: rectify: 0.011 disparity: 0.132 borders: 0.004 filter: 0.004 fill: 0
[CPU] estimating 117x270x96 disparity using 117x270x8u tiles
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[GPU] estimating 139x253x96 disparity using 139x253x8u tiles
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[CPU] estimating 141x227x96 disparity using 141x227x8u tiles
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[CPU] estimating 185x206x96 disparity using 185x206x8u tiles
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[CPU] estimating 258x185x96 disparity using 258x185x8u tiles
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[CPU] estimating 270x196x96 disparity using 270x196x8u tiles
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[CPU] estimating 178x234x96 disparity using 178x234x8u tiles
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[CPU] estimating 138x203x96 disparity using 138x203x8u tiles
timings: rectify: 0.003 disparity: 0.062 borders: 0.003 filter: 0.003 fill: 0
[CPU] estimating 94x144x96 disparity using 94x144x8u tiles
timings: rectify: 0.002 disparity: 0.038 borders: 0.001 filter: 0.001 fill: 0
[CPU] estimating 51x57x96 disparity using 51x57x8u tiles
timings: rectify: 0.001 disparity: 0.007 borders: 0 filter: 0 fill: 0

Depth reconstruction devices performance:
 - 5%    done by CPU
 - 27%    done by Intel(R) HD Graphics 530
 - 69%    done by Quadro M2000M
Total time: 410.602 seconds

Generating dense cloud...
Generating dense point cloud...
selected 998 cameras in 0.817 sec
working volume: 0x0x0
tiles: 1x1x1
selected 998 cameras in 0.007 sec
preloading data... done in 0.786 sec
filtering depth maps... done in 3128.16 sec
preloading data... done in 40.677 sec
working volume: 0x0x0
tiles: 1x1x1
accumulating data...

This report also contains technical information about the state of the application when it crashed.