I am having issues with merging projects created using Pix4D Catch and a DJI Drone. The following are steps I used to process the data sets within the software.
Project #1 using Pix4DCatch with iPhone 15 Pro Max:
1.) Import all images into the project.
2.) Download GCP csv. file from Trimble Reveal GNSS.
3.) Select horizontal coordinate reference system (WGS 84 - EPSG:4326)
Select vertical coordinate reference system (EGM96 height - EPSG:5773)
Assign which columns from the imported csv. file are latitude, longitude, and altitude.
4.) Tie the GCP to individual photos within the project.
5.) Manually enter the altitude within the GCP to match with project #2.
6.) Run calibration of the photos.
7.) Once calibration is completed and all photos and GCP are accepted within the project, save the project.
Project #2 using DJI Mavic Aerial Photos:
1.) Import all images into the project.
2.) Download GCP csv. file.
3.) Select horizontal coordinate reference system (WGS 84 - EPSG:4326)
Select vertical coordinate reference system (EGM96 height - EPSG:5773)
Assign which columns from the imported csv. file are latitude, longitude, and altitude.
4.) Tie the GCP to individual photos within the project.
5.) Manually enter the altitude within the GCP to match with project #2.
6.) Run calibration of the photos.
7.) Once calibration is completed and all photos and GCP are accepted within the project, save the project.
8.) Merge the projects together making project #2 the primary project.
I attached a screen shot as a reference when I merge the projects together.
Have you reviewed our technical support document on how to merge projects in PIX4Dmatic? It detail the steps.
I think you need to register the tie points. This step is optional, but since it appears that you have common GCPs between the projects, it will snap the GCPs together and align the projects.
Please take a look at the article above. It should walk you through the steps.
I read this document, and everything in it I have done. I just updated to version 1.74 from 1.71 hoping this may help and it has, but there are still some issues when inspecting the project closely. The vehicle and nearby terrain are slightly offset from each other. I added 4 tie points on the vehicle between the two projects hoping this would help, but when I re-calibrated the project, several photos now will not calibrate. I will attach photos for you as a reference.
Thanks for your willingness to look into this for me. I remapped the scene using Catch and a DJI Drone, except this time I added additional ground targets around the car and magnetic tiles on the car hoping to get more accurate manual tie points. This seems to have helped but the two projects still are not synced perfectly. They’re roughly a foot off, which you can see on the framing of the vehicle in the Mesh data. Below I have attached the quality report for the first and newest project I just completed along with a few screen shots for you to see.
I finally got the two projects to merge properly after several trial and error attempts. What it ultimately boiled down to was the path of travel when flying the drone. Due to the several changes in elevation with the landscape and tree canopies I believe I found the best way to get around this. Photos linked below of the final merged project.
my deep respect for the research you are doing!! Looks like you found a workflow to get this right.
Can you please describe that in detail (although I think this would be the job of the support team here…) How many MTPs have you used to nail these two data sets?
Another question, have you tried it in a different software? Normally it shouldn’t be so much effort on the user side to simply align images from two different sensors… both with RTK data
Did the support give you more information than the information above? Have they given you some useful advice after checking the quality report? Following this conversation here it looks like the support doesn’t really know an answer or how to help…
This project took several attempts of trial and error. I ultimately ended up using 4 ground targets around the car along with 11 April Tags, which are magnetic tiles each with a unique design, to attach on the sides and roof of the car. I attached manual tie points to each ground target and several of the April tags in both projects. I later found using only the 4 ground targets were sufficient enough.
I ran the data sets in Reality Capture and FARO SCENE and had no issues processing the data. This only seemed to happen in Pix.
I sent the quality reports as they requested and never heard anything back or any solutions to solve the problem.
If you want further detail as to how I precisely gathered the data and processed them let me know and I’ll be glad to help.
Product support doesn’t solve the problem and isn’t answering since 10 days - so the users have to find a complex way for solving it
Frankly, and from a user’s point of view, this is disappointing.
so my conclusion: Right now it looks like I can’t combine RTK data sets from PIX4Dcatch and the drone without having a lot of effort (and therefore costs) in PIX4Dmatic. Contacting PIX4D support makes even more work and in the end you have to solve the problem yourself, without feedback from the supports…
Just for us users: If you somehow could do a step by step description what solved this issues in your case in detail I woud be very very thankful Tylor.
and to be fair, can you rule out errors in your data acquisition?
Merging projects can be done using MTPs/GCPs to register the two projects. You will need at least three points. However, you can merge projects without any MTPs/GCPs. In this case, the two merged projects will need to be high accurate RTK/PPK projects that align perfectly.
If there are problems in alignment when merging projects that use MTPs/GCPs, then it is likely a calibration problem from the sub-projects. You need to make sure you have green checkmarks in the quality check before trying to merge.
Lastly, after a project is merged, don’t run the calibration step again as it will defeat the purpose of merging in the first place. Merging allows you to have tailored calibration settings for each sub-project. You should then start the densification.
Sorry for the late reply. Below I have attached a step-by-step process for capturing then processing the data sets.
Data Gathering Pix4DCatch:
Perform a walk through a determine what object(s) need to be captured. Check surroundings for any potential (hazards) that might interfere with the data sets, i.e. trees, powerlines, any moving vehicles/persons, etc.
Place all GCP, ground targets, and April tags that will be used to merge the two projects together. These items can NOT be moved once they are placed so be careful traversing through your scene. In this case, I placed ground targets perpendicular to all four tires and April tags on the doors, trunk, roof, and hood.
Capture all relevant scene information you want to gather using Pix4DCatch. Be sure you have several photos of the ground targets at a 90-degree angle to ensure better accuracy when attaching MTP’s later on.
Once you have gathered all data using Pix4DCatch, check the projects point cloud and ensure you have all the data clear. Sometimes the data will appear in (layers) or will appear to have duplicates. Try gathering more data of these items needing attention and check it again. If this does not work, the project may have to be recaptured.
Once you are satisfied with the data set using Pix4DCatch. Save the project either directly to the device or to the Cloud.
Data Gathering DJI Mavic Drone:
Keeping the hazards seen earlier in mind during the initial walkthrough, develop a flight plan for your drone. For the best detail and time consideration, I have found an elevation of 75ft to work best.
Fly your drone in whichever grid pattern you decide but ensure your camera is at 90 degrees when flying said grid pattern. Once the grid has been flown, you need to capture some oblique photos at roughly 60-75 degrees gimbal tilt. This was crucial in this particular project due to the vehicle being underneath a tree canopy. The ground targets were almost impossible to see when flying solely at 90 degrees.
Keep in mind also, you need to capture your photos with the drone in a geological order. If you make any turns with the drone, ensure you have at least 80% overlapping or the photos will not calibrate when entered into Pix4DMatic. If you change your elevation, roughly every 15-20 feet you need to capture a photo until you reach your desired altitude, if not you will also experience calibration issues.
Once you are satisfied with the data set captured with the drone, land it.
Entering Datasets into Pix4DMatic:
Import your data sets into two separate projects. One with the Pix4DCatch files, the other with the drone photos.
After ensuring all data has been imported correctly, establish your desired CRS (Coordinate Reference System). Ensure the CRS is the same in both projects.
In the drone project, import the data for the GCP’s you mapped on the scene from whichever mapping device you used, in this case I used Trimble Forensics Capture GNSS system.
In the drone project, link the GCP’s within your imported photos.
Attach manual tie points to the ground targets and April tags. When doing this be sure you use the exact location on every item used to create the MTP’s within BOTH projects. I have found it useful to do this chronologically within each project so when I go to merge, they are already number accordingly. EX: MTP1, MTP2, and so on. Ensure your reprojection error and position error are minimal for each MTP. You will need a minimum of 4 marks for each MTP created.
Run the calibration step. Once completed ensure all photos have been processed correctly and have no errors.
If no errors are discovered after processing the calibration. Save both projects.
Merging Projects:
Open whichever project you wish to be considered the “parent” project, it doesn’t particularly matter which one you use. Once opened, select File, Merge Projects.
A window will open for you to select which project you want to merge.
Once you attach the project, select next. This will prompt you to link all the MTP’s you created between the two projects. Once all MTP’s are linked, on the bottom right of this window you will see a reprojection error rating. The lower this rating is the better the chance of the projects merging correctly. If you notice a MTP suddenly increases this error rating drastically, you may need to delete it.
Once satisfied, click next which will prompt you to review the datasets. Ensure “Merge into new project” is selected and click merge.
Once completed you will need to reoptimize the project, allowing the images to better align.
If satisfied with the results after reoptimizing, continue to process the rest of the data.
If there’s anything else I can help you with let me know. I hope this helps.
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both with RTK data 
