| Good question! So why $work-1 spent so much time on this was quite logical. When you have point clouds generated from crappy head mounted cameras, you get models that are very complex. for example, if you look at a point cloud of an Ikea LACK (https://www.ikea.com/gb/en/p/lack-nest-of-tables-set-of-2-wh...) It will be massively complex. this means that when you want to perform nay kind of interaction with it, its computationally difficult (https://www.researchgate.net/publication/221064696/figure/fi...) So an active area of research is point cloud to "CAD" model (ie simplyfied, where a LACK tabl would be ~40 triangles rather than 400k) One of those ways is to say "oh this pointcloud looks like a table, lets generate a bunch of hypothesis tables and see if they fit." One way to do that is to have a model that understands parametric CAD, and can create a number of tables with parameters that can be adjusted until it fits. A perhaps easier way is to take a point cloud, get an image model trained on CAD models to draw models, in 2d imagery, then use something like this to get an actual model out. Its not efficient, but it might work. There are also lots of other cases, like automatic plagiarism, which are less good. |
Basically leverage the randomness to create many variations, then select the most accurate variation automatically.
Terribly wasteful of time and processing power, but so is using GPU time to make pretty pictures randomly.