Over the last decade, the recognition of the potential value of augmented reality (AR) and other human-machine interfaces has been growing. These applications are all based on depth sensing technologies. Among various depth sensing technologies, the Time-of-Flight (ToF) approach is emerging as a widely applicable method because it has the potential of reaching much longer distances at higher speed and accuracy, is natively suitable for mobile phones or AR.
One obvious problem is that the dToF system mainly targets the automotive industry or 3D imaging in close range, which means different power, accuracy, and fewer area requirements than mobile phone or AR applications. The application of the dToF system in the mobile phone or AR industry needs to be tested. The data volume is another problem of dToF. Usually, in the read out part, the dToF time-to digital converter (TDC)’s timestamps will all be saved into a histogram. The peak value of the histogram is the detected result. However, in a large pixels scenario, the area cost will be too much for mobile phone or AR applications if the whole histogram for all pixels is saved. Hence, an algorithm that can save histogram partially or find out the peak value without saving histogram is needed.
This thesis proposed two novel algorithms for the dToF system’s read out part and tested three other algorithms’ functions in mobile phone or AR applications. With the Lambertian model and probabilistic theory, a module of the dToF system is built using MATLAB to generate a testing dataset. Besides, introductions to depth-sensing technologies, single-photon avalanche diode (SPAD) sensors, and dToF systems will be given before the core chapters of the thesis.