Thanks for all the help/information on previous projects - the input has been very useful.
I seem to have hit a bit of a snag with a current project I am working on. We conducted an aerial mapping survey flight (control used with the point cloud projected in an arbitrary/project specific coordinate system) as well as manual flight operations for capturing the roofing system - copper roof (both aged and new areas). The data sets were captured using 2 different drones - mapping flight was captured at 100M AGL using an Intel Falcoin 8+ (Sony Alpha A7r) and the manual flight ops for the roof using a DJI Mavic 2 Pro at a consistent distance between 2-8M away from the roofing systems.
The data sets were processed separately through Stage 1 and the proper process for merging projects was done. When the data was merged and GCP accuracy was verified with checkpoints I ran the project through stage 2 for Point cloud generation. When completed the density of the points for the copper roof was lackluster at best - large areas with gaps. I know reflective surfaces have issues with processing but was hoping that the topo data would supplement the areas that may have required more overlap. However it appears to have done the opposite. I am wondering if there is something that I missed in terms of Advanced processing options or camera model options that I may have missed?
Roof Data - No Merge
Roof Data Merged with Topo
I decided to process just the roofing system on its own to see whether or not I would have better results with the point density and was surprised to see how much better the single data set was as compared to the merge. I am hoping that I do not have to recapture/re-deploy the areas and work with the single data set. Looking forward to some input. Cheers
Thank you kindly for reaching out/taking the time. I am uploading the reports now. The full merge report will look a bit different as it is an arbitrary coordinate system that is project specific… In terms of how I processed the data for the roofing system I processed each elevation individually (sometimes split into 2 sub projects) and then merged to create the full roof as a whole. Let me know if you have any insight/tweeks I can do as opposed to rescanning the areas to increase overlap - I am hoping to avoid this.
Thank you for uploading the quality reports. These are helpful to evaluate the projects.
Before merging the project, it is important to check the accuracy of the projects and set the same Coordinate system (both horizontal and vertical) of the images and the output of projects. When you merge the project, please make sure that the coordinate system of projects are the same and there are two camera models.
You can select one camera model by assigning all the images on Image Properties Editor. Please find more information here.
Lastly, you might want to process with a lower Keypoints Image Scale. This processing option can lead to a higher number of calibrated images than the default original keypoint image scale. For more information: Menu Process > Processing Options… > 1. Initial Processing > General.
Thank you kindly for the information - it is greatly appreciated. The reason I increased the image scale is I was hoping for a larger number of keypoints/point cloud points after stage 2. I have processed the data several times under different parameters and the QRs I provided were from the most recent processed projects.
I was under the impression that coordinate systems were a bit of a moot point when processing multiple projects so long as there was GCP information for one - I thought the software would ultimately default to the coordinate system of the project with GCPs. That is good to know for future projects.
I will make the adjustments for the camera models - will take a while with 5k+ images but I will try that today. Do you think that this will have a major impact on the number of points/gaps i am seeing? Also am I to make these adjustments PRE merge or can I do so on the merged projects and restart stage 2?
Thank you for the update.
Regarding the Keypoints Image Scale, you could adjust it based on the quality report. In the Quality Check table of the quality report, the Dataset raw has a yellow sign which means that between 60% and 95% of enabled images are calibrated or more than 95% of enabled images are calibrated in multiple blocks. If the dataset is Repetitive or complex, processing with a lower Keypoints Image Scale could improve the result. For more information, please visit here.
The coordinate systems could cause issues as you mentioned. It is always better if you have the same coordinate systems.
It would be difficult to confirm that the adjustments of the camera models would have a major impact on the results; however, it would be better if you adjust pre merged project even if this may not have significant impacts on the result.
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