The recent development of single-photon avalanche diode (SPADs) arrays as imaging sensors with both picosecond binning capabilities and single photon sensitivity has led to the rapid development of time-of-flight imaging systems however, simulations of SPAD systems outside of the Poisson regime remain rare. Here we present a model for SPAD systems which combines single photon counting statistics with computational parallelization which together enable the efficient generation of photo-realistic SPAD data. We confirm the accuracy of out model by experimental verification. Further, we apply this simulator to the problem of drone identification, orientation, and, segmentation. The proliferation of semi-autonomous aerial multi-copters i.e. drones, has raised concerns over the ability of existing aerial detection systems to accurately characterize such vehicles. Here, we fuse the 3D imaging of SPAD sensors with the classification capabilities of a bespoke convolutional neural network (CNN) into a system capable of determining drone pose in flight. To overcome the lack of publicly available training data we generate a photo-realistic dataset to enable the training of our network. After training, we are able to predict the roll, pitch, and yaw of the several different drone types with an accuracy greater than 90%.
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