(plain black-on-white slides are Evan’s). Dense NRSFM Approach Overview.

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Presentation transcript:

(plain black-on-white slides are Evan’s)

Dense NRSFM Approach Overview

Multi-Frame Optical Flow Details Set of points to be tracked comes from a reference frame Represent trajectories with a linear basis Penalties for deviating from the low-rank structure Spatial pyramid = Garg, Roussos, Agapito, “Robust Trajectory-Space TV-L1 Optical Flow for Nonrigid Sequences”, EMMCVPR11

Orthographic Projection Orthographic: Perspective: Requires small variation in depth u = s u x + u c v = s v y + v c u = (x – x c ) f / z + u c v = (y – y c ) f / z + v c linear in (x, y, z) nonlinear in (x, y, z)

Optimizing for 3-D Shape becomes

Qualitative Results [online videos]

Quantitative Comparison Top row: ground truth; bottom row: their result RMS reconstruction error, with matrix rank in parens