Author Topic: Doming with target detection  (Read 1363 times)


  • Newbie
  • *
  • Posts: 4
    • View Profile
Doming with target detection
« on: June 04, 2018, 07:40:41 PM »
We are using an updated automatic batch processing script to process large amounts of data. We have success with target detection but the errors on the targets are huge. We are also experiencing the well documented 'doming' effect.

Moving from version 1.1 to 1.4, I want to make sure we are 'optimizing' the photo alignment and cameras properly. After photo alignment and target detection, we are using 'optimizeCameras'

Match photos (perform image matching for the chunk) and align camera (perform photo alignment for chunk)
    chunk.matchPhotos(accuracy=PhotoScan.HighAccuracy, generic_preselection=True)

### Detect markers
    chunk.detectMarkers(type=PhotoScan.CircularTarget12bit, tolerance=80) 
    chunk.loadReference(coord_path, format=PhotoScan.ReferenceFormatCSV, skip_rows=1, columns="nxyz",
                        delimiter=",")  # you can alter the columns order and delimiter according to the input csv format
    chunk.marker_location_accuracy = PhotoScan.Vector(
        [0.001, 0.001, 0.001])  # you can input different accuracy along every axis

    print('Fini les cilbes debut seuil')
    print('debut optimisation')
### Perform optimization of point cloud/camera parameters

Help is greatly appreciated!