General Defocusing Particle Tracking (GDPT) is a single-camera,
three-dimensional particle tracking method that determines the particle depth
positions from the defocusing patterns of the corresponding particle images.
GDPT relies on a reference set of experimental particle images which is used to
predict the depth position of measured particle images of similar shape. While
several implementations of the method are possible, its accuracy is ultimately
limited by some intrinsic properties of the acquired data, such as the
signal-to-noise ratio, the particle concentration, as well as the
characteristics of the defocusing patterns. GDPT has been applied in different
fields by different research groups, however, a deeper description and analysis
of the method fundamentals has hitherto not been available. In this work, we
first identity the fundamental elements that characterize a GDPT measurement.
Afterwards, we present a standardized framework based on synthetic images to
assess the performance of GDPT implementations in terms of measurement
uncertainty and relative number of measured particles. Finally, we provide
guidelines to assess the uncertainty of experimental GDPT measurements, where
true values are not accessible and additional image aberrations can lead to
bias errors. The data were processed using DefocusTracker, an open-source GDPT
software. The datasets were created using the synthetic image generator
MicroSIG and have been shared in a freely-accessible repository
