from Hacker News

Beyond the pixel plane: sensing and learning in 3D

by gtmtg on 8/25/18, 6:12 PM with 15 comments

  • by JeremyHerrman on 8/26/18, 12:20 AM

    This is an exciting time for those of us working on computational geometry to better understand 3D shapes across many industries.

    In addition to the architectures mentioned in this great overview, I'm excited to see progress on spectral and geodesic CNNs for graphs and manifolds. Check out this other fantastic source for info on 3D ML: http://geometricdeeplearning.com

  • by jeffchuber on 8/25/18, 8:24 PM

    This is a great overview. Also checkout CS 468 from Stanford, http://graphics.stanford.edu/courses/cs468-17-spring/ "Machine Learning for 3D Data"

    Also, if you want to work on this stuff full time- https://news.ycombinator.com/item?id=17649726

  • by glalonde on 8/26/18, 1:12 AM

    What about pose estimation? e.g. Given a well defined coordinate system, like the origin is the nose on a face, determine the pose of the face. Is this still best done with classic optimization formulations like ransac/ICP and a supplied model, or have these been bested by learned models somehow?

  • by ajmarcic on 8/26/18, 12:15 AM

    Has any progress been made towards single view 2D -> 3D inference?