Simulation-Based Estimation of the Number of Cameras Required for 3D Reconstruction in a Narrow-Baseline Multi-Camera Setup

Abstract

Graphical visualization systems are a common clinical tool for displaying digital images and three-dimensional volumetric data. These systems provide a broad spectrum of information to support physicians in their clinical routine. For example, the field of radiology enjoys unrestricted options for interaction with the data, since information is pre-recorded and available entirely in digital form. However, some fields, such as microsurgery, do not benefit from this yet. Microscopes, endoscopes, and laparoscopes show the surgical site as it is. To allow free data manipulation and information fusion, 3D digitization of surgical sites is required. We aimed to find the number of cameras needed to add this functionality to surgical microscopes. For this, we performed in silico simulations of the 3D reconstruction of representative models of microsurgical sites with different numbers of cameras in narrow-baseline setups. Our results show that eight independent camera views are preferable, while at least four are necessary for a digital surgical site. In most cases, eight cameras allow the reconstruction of over 99% of the visible part. With four cameras, still over 95% can be achieved. This answers one of the key questions for the development of a prototype microscope. In future, such a system can provide functionality which is unattainable today.

Standardized 3D test object for multi-camera calibration during animal pose capture

“…Similarly, the median errors in the larger arena using the two- and three-camera configurations were 4.56 and 4.24 mm, respectively. This finding confirms that increasing the number of camera views can improve the accuracy and robustness of 3D reconstruction systems 16 . Interestingly, the use of filters was observed to have no significant effect on either the bias and variance of the aggregate error for test 3D printed objects [Figs.…”

“…This finding confirms that increasing the number of camera views can improve the accuracy and robustness of 3D reconstruction systems. 16 Interestingly, the use of filters was observed to have no significant effect on either the bias and variance of the aggregate error for test 3D printed objects [ Figs. 2(b) and 2(d) ].…”