Interactive Camera Network Design using a Virtual Reality Interface

Traditional literature on camera network design focuses on constructing automated algorithms. These require problem specific input from experts in order to produce their output. The nature of the required input is highly unintuitive leading to an unpractical workflow for human operators. In this work we focus on developing a virtual reality user interface allowing human operators to manually design camera networks in an intuitive manner. From real world practical examples we conclude that the camera networks designed using this interface are highly competitive with, or superior to those generated by automated algorithms, but the associated workflow is much more intuitive and simple. The competitiveness of the human-generated camera networks is remarkable because the structure of the optimization problem is a well known combinatorial NP-hard problem. These results indicate that human operators can be used in challenging geometrical combinatorial optimization problems given an intuitive visualization of the problem.Comment: 11 pages, 8 figure

A Continuous Motion Shape-from-Focus Method for Geometry Measurement during 3D Printing

In 3D printing, as in other manufacturing processes, there is a push for zero-defect manufacturing, mainly to avoid waste. To evaluate the quality of the printed parts during the printing process, an accurate 3D measurement method is required. By scanning the part during the buildup, potential nonconformities to tolerances can be detected early on and the printing process could be adjusted to avoid scrapping the part. Out of many, shape-from-focus, is an accurate method for recovering 3D shapes from objects. However, the state-of-the-art implementation of the method requires the object to be stationary during a measurement. This does not reconcile with the nature of 3D printing, where continuous motion is required for the manufacturing process. This research presents a novel methodology that allows shape-from-focus to be used in a continuous scanning motion, thus making it possible to apply it to the 3D manufacturing process. By controlling the camera trigger and a tunable lens with synchronous signals, a stack of images can be created while the camera or the object is in motion. These images can be re-aligned and then used to create a 3D depth image. The impact on the quality of the 3D measurement was tested by analytically comparing the quality of a scan using the traditional stationary method and of the proposed method to a known reference. The results demonstrate a 1.22% degradation in the measurement error

Camera calibration using gray code

In order to determine camera parameters, a calibration procedure involving the camera recordings of a checkerboard is usually performed. In this paper, we propose an alternative approach that uses Gray-code patterns displayed on an LCD screen. Gray-code patterns allow us to decode 3D location information of points of the LCD screen at every pixel in the camera image. This is in contrast to checkerboard patterns where the number of corresponding locations is limited to the number of checkerboard corners. We show that, for the case of a UEye CMOS camera, the precision of focal-length estimation is 1.5 times more precise than when using a standard calibration with a checkerboard pattern