The binary defocusing technique (BDT) is advantageous in high-speed dynamic three-dimensional (3D) measurement. However, its depth range is limited, since the defocused projection mode determines that it can only obtain high-quality measurement results when the defocusing degree is appropriate. To expand the measurement depth, this study proposes a deep learning-driven binary focusing projection 3D measurement method. The proposed method does not necessarily consider the influence of defocusing degree, thus overcoming the limitations of BDT. Furthermore, a two-stage deep learning framework is designed to process binary fringes. In this framework, the adversarial learning realizes the generation of high-quality sinusoidal fringes in the whole depth range. Moreover, the branch residual learning outputs fringe orders to assist phase unwrapping, reducing the edge jump error caused by the traditional BDT. The experimental results show that the proposed method significantly expands the measurement depth range while maintaining high-quality 3D reconstruction in the whole depth range.