A Novel Method to Improve Quality Surface Coverage in Multi-View Capture

TL;DR

This paper introduces a novel focus optimization method for multi-view capture that enhances surface coverage quality by assigning focus distances to cameras using EM and multi-view algorithms, significantly improving in-focus surface area.

Contribution

The paper presents a new EM-based and a multi-view algorithm to optimize camera focus distances for better surface coverage in multi-view photogrammetry.

Findings

EM and $k$-view algorithms increase in-focus surface area by over 1500 cm$^2$

Methods improve baseline single-view coverage by at least 24% and 28%

Effective in simulations for total body photography

Abstract

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.