Lines Matching full:further
72 Before going further, let us make some notational simplifications. We will assume that the matrix $\sqrt{\mu} D$ has been concatenated at the bottom of the matrix $J$ and similarly a vector of zeros has been added to the bottom of the vector $f$ and the rest of our discussion will be in terms of $J$ and $f$, \ie the linear least squares problem.
166 and then use it as the starting point to further optimize just $a_1$
173 This idea can be further generalized, by not just optimizing $(a_1,
261 Suppose that the SfM problem consists of $p$ cameras and $q$ points and the variable vector $x$ has the block structure $x = [y_{1},\hdots,y_{p},z_{1},\hdots,z_{q}]$. Where, $y$ and $z$ correspond to camera and point parameters, respectively. Further, let the camera blocks be of size $c$ and the point blocks be of size $s$ (for most problems $c$ = $6$--$9$ and $s = 3$). Ceres does not impose any constancy requirement on these block sizes, but choosing them to be constant simplifies the exposition.
549 algorithm. Essentially this amounts to doing a further optimization
