Recent advances in imaging sensors and digital light projection technology have facilitated rapid progress in 3D optical sensing, enabling 3D surfaces of complex-shaped objects to be captured with high resolution and accuracy. Nevertheless, due to the inherent synchronous pattern projection and image acquisition mechanism, the temporal resolution of conventional structured light or fringe projection profilometry (FPP) based 3D imaging methods is still limited to the native detector frame rates. In this work, we demonstrate a new 3D imaging method, termed deep-learning-enabled multiplexed FPP (DLMFPP), that allows to achieve high-resolution and high-speed 3D imaging at near-one-order of magnitude-higher 3D frame rate with conventional low-speed cameras. By encoding temporal information in one multiplexed fringe pattern, DLMFPP harnesses deep neural networks embedded with Fourier transform, phase-shifting and ensemble learning to decompose the pattern and analyze separate fringes, furnishing a high signal-to-noise ratio and a ready-to-implement solution over conventional computational imaging techniques. We demonstrate this method by measuring different types of transient scenes, including rotating fan blades and bullet fired from a toy gun, at kHz using cameras of around 100 Hz. Experiential results establish that DLMFPP allows slow-scan cameras with their known advantages in terms of cost and spatial resolution to be used for high-speed 3D imaging tasks.