Object Complexity

A project log for Photogrammetry and Image Acquisition

An exploration into image acquisition techniques and its effects on the quality of 3D models using Pix4D Mapper Pro software.

travis-broadhurstTravis Broadhurst 07/11/2016 at 11:420 Comments

The complexity of the object is also a crucial component of 3D modeling. Most software that processes photos for 3D modeling is similar to Pix4D mapper in that they recognize similar keypoints in images and match those keypoints. If there are more keypoints, and thus a more complex object, more keypoints can be matched between images and more 3D points can be determined. If there are more 3D points, the triangle mesh will have a better resolution and be more precise, rendering a much more optimal model.

In these trials, which are mostly taken from an public architecture model display in Konstanz, display how the complexity of an object can detract or add to a model, even with a similar photo acquisition method. The architecture model display included over 50 models of different buildings and city plans from around the world and were all done by the architecture college here in Konstanz. I took pictures and made models of 25 of those examples. All were of varying complexity, and so captured images of each using Oblique photogrammetry. In all the cases observed, the best models were the ones of very complex objects. In many cases, I had to manually calibrate some of the images for the objects that were very uniform in order to even get a mesh that would show the majority of the object. Quantitative and qualitative data for this part of the project are included in the Excel file labeled "Architecture_Photo_Acquisition".

The other trial that is a perfect example of the complexity of the model is of the Bismarkturm. The Bismarkturm is a tower commemorating Otto von Bismark and is located in Konstanz, Germany. I used images from a drone flight of the tower to create a model and the video that is included in the project files. However, the initial images from the drone flight did not render an accurate model even though there were plenty of photos with plenty of overlap. I noticed that many of the photos either included parts of the sky or included too much of the tower within the FOV of the camera. Since the tower was largely featureless, these images were detracting from the keypoint matches and actually detracting from the model. I then only chose the images that included the entire tower and included some of the nearby scenery to add to the complexity of the object. The second attempt was much more successful and produced an accurate model with less than half of the images originally used.

The FOV trials also support the observations on complexity that were noticed in the above trials.

Thank you.