I’m working my way towards a similar process where I hope to use 360 terrestrial images (vehicle mounted Insta 360 Pro 2) combined with traditional UAV imagery to create 3D models of downtown areas. However, I’m still running into issues processing any/all 360 images.
I’m currently using stitched (equirectangular) images but always end up with a nonsensical pointcloud, it looks like it exploded. The tutorials available make 360 image processing seem very straight forward but we’ve never had much luck.
Hoping for some pointers as to where my Initial Processing is going wrong.
The most important thing regarding the spherical cameras are the way you are capturing the images, the overlap and the light.
Here is an article describing how to process a spherical project in PIX4Dmapper and an example project the goal of which is to generate the 3D model of a church taken by an NCTech iSTAR spherical camera:
This is an article I’ve read a handful of times to try and put together a processing workflow. What I’m running into is that even though the images are geolocated the software has a lot of issues matching. The images are located accurately in Map View and have been stitched into the correct format.
I’d like to plan a re-drive in this same area. The end goal is to use vehicle-mounted 360 cameras to capture building facades and streets in downtown areas where we can’t fly at low altitudes. Then, we’ll use a parallel drone flight for the rooftops.
This will (hopefully) but used for generating an accurate mesh that we can later 3D print.
Are you familiar with any workflows or missions similar to this? The current guide for processing with 360 images doesn’t provide very much in terms of trouble shooting.
From the quality report, it appears that you definitely need more images, as the overlap is insufficient. I would suggest experimenting with PIX4Dcatch to compare the frequency of capture, plus an extra dataset could be helpful as well. What kind of distance are we considering for these captures? I would suggest starting with smaller distances and then building up.
When upping the frequency of images taken, consideration should be put into the speed of the vehicle so as to not go too fast to avoid motion-blurred images, as these might not calibrate. It would also be advised to time your capture during times of reduced traffic(vehicle and pedestrian) as moving objects within the subject matter can cause issues calibrating as well. With sufficient images and good overlap, you will be able to get the images to tie together. The trick will be to have enough overlapping image content between the terrestrial and aerial datasets to tie them together. How tall are the buildings? Do you have GCPs/MTPs?
Thanks for the response. The best we can reasonable do is 40-50’ per image, and we’re going to start collecting both sides of the street for some higher density coverage.
For now, we’re doing 2s intervals at either 10 or 15mph in the vehicle. Ideally, we’ll be able to set control points, fly, and collect with the vehicle mounted camera all in one day. Our typical buildings will only be 2-stories, I’d have to double check the actual heights.
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