In vision and machine learning, almost everything we do may be considered to be a form of model fitting. Whether estimating the parameters of a convolutional neural network, computing structure and motion from image collections, tracking objects in video, computing low-dimensional representations of datasets, estimating parameters for an inference model such as Markov random fields, or extracting shape spaces such as active appearance models, it almost always boils down to minimizing an objective containing some parameters of interest as well as some latent or nuisance parameters. This tutorial will describe several tools and techniques for solving such optimization problems, with a focus on fitting 3D smooth-surface models, such as subdivision surfaces, to 2D and 3D data.
Note: We will stop for questions at any time, and may choose to go slower or faster over some points, and we reserve the right to slip some topics across session boundaries. This means that if you want to attend just one specific session, you might want to allow a 15-30 minute buffer afterwards.
0900 Intro: Applications in vision and graphics.
0920 Session I: Matrix and vector calculus, nonlinear optimization
1045 Session II: Curves and Correspondences
1140 Break and stretch
1145 Session III: Surfaces
1400 Session IV: Robustness and speed
1515 Session V: Software
1615 More coffee, more stretching
1630 Session VI: Conclusions, open problems, misc…