Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as “wiggling”. Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance.

Imaging 3D measurement systems differ not only in design, power consumption, and resolution but, most notably, in their measurement performance, which is rarely documented in a reproducible or comparable manner. In this work, we introduce a simple, universal methodology for quantitatively characterizing iToF cameras, focusing on point cloud data acquired under reproducible conditions. Our metrics build upon the ISO 5725-1 standard and related research to enable standardized comparisons across different systems, independent of their measurement principles. Beyond trueness, accuracy, and precision, we propose additional metrics to evaluate surface texture quality, addressing visually disruptive systematic measurement errors. We condense these measurements into 14 key metrics for a straightforward camera comparison. In addition, we introduce the Energy-Density-Weighted Depth Precision Index EPI as a metric to relate an active measurement systems performance to the emitted optical power. Finally, we demonstrate the applicability of our methodology by evaluating seven state-of-the-art 3D systems.