I have taken a series of overlapping oblique images of a building facade with an iPhone camera (with geotags) and generated a point cloud and orthomosaic in Pix4D. When I import the point cloud and orthomosaic into eCognition, it shows that the point at the ground adjacent part of the facade has a lower Z value and the point of the upper part has a higher Z value but I need “camera to facade distance” as Z value to help extract cracks on the facade using OBIA in eCognition.
Later I generated point cloud and mosaic by deleting geolocation information and selected “Arbitrary”
(output) coordinates in Pix4D. Still, the Z value is the height above the ellipsoid (according to WGS 1984), not the distance from camera position to facade. What can I do?!
By changing orientation while taking orientation constraints, I got the “Camera to facade distance” which varies object to object (object depths) on facade. But it shows in negative numbers.
Is there anything to do to get the distance in positive numbers?
One thing you could try is to add some arbitrary GCPs that will give an arbitrary coordinate system to your facade. For example, you could add a GCP at the lower left part of your facade with coordinates 0,0,0.
Similarly, you should add more GCPs so that the numbers come out positive.
I believe the numbers came out negative because you removed the image geolocation and this lead to an arbitrary coordinate system.
Yes, you are right. I have removed the image geolocation and taken orientation and scale constraints.
I understand that I can add the coordinates 0,0,0 at the lower left part of my facade as a GCP.
If I add another GCP at the lower right corner, the coordinates should not be 0,0,0 I guess. So, how to find out the coordinates of that point?
One thing I can do: after inserting 0,0,0 coordinates at the lower left corner, I can check on the facade image what coordinates at the lower right corner come up.
Then, I can add that as its GCP. Does that make sense?
GCP1 (0,0,0)
GCP2 (X,0,0)
Where X is the real distance between point 1 and 2 in one direction
GCP3 (X,Y,Z): a point in the direction of the camera and the facade in order to define the Z direction
I hope it makes sense
Given that this is an application that is not officially supported by Pix4Dmapper, unfortunately, I cannot give further assistance.
Currently, the software does not export “camera to facade distance” as Z value by default.
But you can give it a try with arbitrary GCPs. I’ve seen similar projects in the past from other customers.
This email finds you well. Thanks for your continued cooperation.
I have some problems generating good reconstruction in Pix4D. I need your help soon.
You know that I am working with building facades (oblique photogrammetry). I capture images with an iPhone camera.
I see it is very difficult to generate a good reconstruction. What is the problem I don’t know? I take images very carefully with much overlap. After the acquisition, I check the images on the laptop one by one and delete the blurry images. Despite that, I do not get a good reconstruction of the facade. It is giving me much pain killing my valuable time. I am attaching some of my experiment results for your reference.
Another problem I am facing is: to change the orientation of a project, when I remove geolocation, take orientation constraints and then, reoptimize the project, orientation constraints do not work, sometimes I see them displaced (you will see in the attachment).
What are the possible reasons for this? How to solve this problem?
In the link Christina suggested on how to orient the project, it is written that if the project has images with geolocation and the Orientation Constraint is too different from the orientation given by the image geolocation, the Orientation Constraint will not be taken into account. When deleting the geolocation of the images without reoptimizing or running Step 1. again, this action is not taken into account.
I would suggest removing image geolocation , running Step 1., adding orientation constraints and then reoptimize.
The orientation given by the image geolocations is very different from the one I need. So, I am going to follow your suggestion “removing image geolocation, running Step 1., adding orientation constraints and then reoptimize”.
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