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A lot of the CV problems are essentially 'solved', though. Feature extraction (AKAZE, BinBoost), matching (GPU brute force hamming), RANSAC (with PROSAC and re
by snovv_crash 10y ago
A lot of the CV problems are essentially 'solved', though.
Feature extraction (AKAZE, BinBoost), matching (GPU brute force hamming), RANSAC (with PROSAC and relatives), bundle adjustment (Schur Decomposition with LM), point clouds (SGM, PatchMatch) and mesh (Poisson, FSSR).
Each stage has tuning, and real-time requires sacrifices on the hardware we have available, but we know with better computers we can have it. (HSA has me drooling, hurry up with Zen, AMD!)
IMO the harder stuff is in semantic segmentation of point clouds and dynamic scenes, but I have high hopes for the next few years.
- AndrewKemendo 10y agosemantic segmentation of point clouds lol, hey it's hard enough to do with static images. Feature matching pointclouds is probably turing complete :P. That's the kind of shit we're working on though. We're trying to turn the real world into a platform.
- snovv_crash 10y agoI feel like it should be easier than images, since we have all the 3D information. There are all of the features based on 3D structure which image segmenters can't even begin to use. It's one of those things that we can do efficiently, so with enough priors of what scenes look like a sufficiently informed ML system should be able to get decent accuracy.