I have been trying for a while to put irregular things (point clouds) in regular grids, to use the grid itself as a data structure. Not because it is better, but just because grids are more structured than e.g. trees and thus easier integrate into ML pipelines. It all started here, with simple multi-indexing:
Then I realised that to make the multi-indexing procedure more effective, I need some kind of preprocessing, to make the points flat (2d insteed of 3d). When they are already flat I can sort them better.
Then I heard about mesh flattening, https://geometrycollective.github.io/boundary-first-flatteni...
geometry images https://hhoppe.com/proj/gim/ and thought: this is something I need to try with point clouds, whenever they have a latent surfacic distribution. And well, it seems to work. At least now I can have bunnys dragon and elephants all to fit in the grid and not getting half of the legs, ears or whatever lost in the process.
I have been trying for a while to put irregular things (point clouds) in regular grids, to use the grid itself as a data structure. Not because it is better, but just because grids are more structured than e.g. trees and thus easier integrate into ML pipelines. It all started here, with simple multi-indexing:
https://news.ycombinator.com/item?id=49042861
Then I realised that to make the multi-indexing procedure more effective, I need some kind of preprocessing, to make the points flat (2d insteed of 3d). When they are already flat I can sort them better.
Then I heard about mesh flattening, https://geometrycollective.github.io/boundary-first-flatteni... geometry images https://hhoppe.com/proj/gim/ and thought: this is something I need to try with point clouds, whenever they have a latent surfacic distribution. And well, it seems to work. At least now I can have bunnys dragon and elephants all to fit in the grid and not getting half of the legs, ears or whatever lost in the process.