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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.

reposted from here https://stackoverflow.com/questions/58757853/deep-learning-for-3d-point-clouds-volume-detection-and-meshing


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.

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    $\begingroup$ Are you sure this is the right place to ask about "AI" but then i suspect that you need no "AI" $\endgroup$ – joojaa Nov 8 at 15:55
  • $\begingroup$ In another post I was recommended this group, apologies if this is the wrong place I'm not after "AI" just machine learning / deep learning direction, I don't want to reinvent the wheel. $\endgroup$ – Spatial Digger Nov 8 at 22:08
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    $\begingroup$ Thats fine, however you may consider explsining the nature of the data better. $\endgroup$ – joojaa Nov 9 at 6:21
  • $\begingroup$ @joojaa does that help? It is a fairly logical problem, we know the date and time of each point cloud, the latter the date/time the deeper the data, or inversely the lower the point cloud the later it was captured. each point cloud is accurately georeferenced +/- up to 3cm (dgps) they are all within a 10x10x3 volume. So classifying the points as A) part of the volume, B) overlapping points, C) outlying data points. $\endgroup$ – Spatial Digger Nov 10 at 20:54
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    $\begingroup$ sounds like a XY problem. But your not describing yourself very well. In either case if you persist in asking deep learning solutions then you are in the wrong forum. Dont specify the solution just describe the problem, youll get better answers. Why would there be overlapping points? I can guess but . Volume classifying should be trivially just the distance to a datum of some kind or just each pointcloud. Outlying datapoints should be handled by the poissob disk surface reconstruction. Anyway i am afraid you have consumed any interest I have in your problem. $\endgroup$ – joojaa Nov 11 at 6:15

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