A deep neural network provides real-time pose estimation by combining two custom deep neural networks, a location classifier and an ID classifier, with a pose estimation algorithm to achieve a 6D0F location of a fiducial marker. The locations may be further refined into subpixel coordinates using another deep neural network. The networks may be trained using a combination of auto-labeled videos of the target marker, synthetic subpixel corner data, and/or extreme data augmentation. The deep neural network provides improved pose estimations particularly in challenging low-light, high-motion, and/or high-blur scenarios.