Modeling, analysis, and optimization of random error in indirect time-of-flight camera

For indirect time-of-flight (iToF) cameras, we proposed a modeling approach focused on addressing random error. Our model characterizes random error comprehensively by detailing the propagation of error introduced by signal light, ambient light, and dark noise through phase calculation and system correction processes. This framework leverages correlations between incident light and tap responses to quantify noise impacts accurately. We then experimentally validated the theoretical model, confirming its predictive accuracy. Additionally, from a waveform design perspective, we recommend selecting an optimal duty cycle for the light waveform based on the relative intensities of ambient and signal light to effectively reduce random error.