  The depth of field of a camera is a limiting factor for applications that
require taking images at a short subject-to-camera distance or using a large
focal length, such as total body photography, archaeology, and other
close-range photogrammetry applications. Furthermore, in multi-view capture,
where the target is larger than the camera's field of view, an efficient way to
optimize surface coverage captured with quality remains a challenge. Given the
3D mesh of the target object and camera poses, we propose a novel method to
derive a focus distance for each camera that optimizes the quality of the
covered surface area. We first design an Expectation-Minimization (EM)
algorithm to assign points on the mesh uniquely to cameras and then solve for a
focus distance for each camera given the associated point set. We further
improve the quality surface coverage by proposing a $k$-view algorithm that
solves for the points assignment and focus distances by considering multiple
views simultaneously. We demonstrate the effectiveness of the proposed method
under various simulations for total body photography. The EM and $k$-view
algorithms improve the relative cost of the baseline single-view methods by at
least $24$% and $28$% respectively, corresponding to increasing the in-focus
surface area by roughly $1550$ cm$^2$ and $1780$ cm$^2$. We believe the
algorithms can be useful in a number of vision applications that require
photogrammetric details but are limited by the depth of field.
