I'm working (as a Post-doc) on an archaeological excavation point cloud dataset with over 2.5 Billion points. This points come from a trench, a cuboid 10 x 10 x 3 m. Each point cloud is a layer, the gaps between are the excavated volumes. There are 444 volumes from this trench, 700 individual point clouds.
Can anyone give me some direction to any deep learning algorithms which can mesh these empty spaces? I'm already doing this semi-automatically using Open3D and other python libraries, but if we could train the program to assess all the point clouds and deduce the volumes it would save us a lot of time and hopefully get better results.
We excavate more trenches every year so the perfect place to test these out.
To clarify the nature of the data, as requested.
We gather a point cloud of the ground surface using Structure from Motion - photogrammetry. We then excavate a layer (e.g remove 10cm of soil across the trench) and record again. This process repeats until we hit bedrock, in this trench it is 3m deep. This is a vastly simplified example since we have fire pits, walls, and irregularly shaped features. each point cloud is between 1 - 20 million points, we are currently using a 1 - 10 % sample of this data and getting good results, however we would like to employ deep learning to eventually save time and produce more accurate results.