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We present a range-gated camera system designed for real-time (10 Hz) 3D estimation underwater. The system uses a fast-shutter CMOS sensor (1280×1024) customized to facilitate gating with 1.67 ns (18.8 cm in water) delay steps relative to the triggering of a solid-state actively Q-switched 532 nm laser. A depth estimation algorithm has been carefully designed to handle the effects of light scattering in water, i.e., forward and backward scattering. The raw range-gated signal is carefully filtered to reduce noise while preserving the signal even in the presence of unwanted backscatter. The resulting signal is proportional to the number of photons that are reflected during a small time unit (range), and objects will show up as peaks in the filtered signal. We present a peak-finding algorithm that is robust to unwanted forward scatter peaks and at the same time can pick out distant peaks that are barely higher than peaks caused by sensor and intensity noise. Super-resolution is achieved by fitting a parabola around the peak, which we show can provide depth precision below 1 cm at high signal levels. We show depth estimation results when scanning a range of 8 m (typically 1-9 m) at 10 Hz. The results are dependent on the water quality. We are capable of estimating depth at distances of over 4.5 attenuation lengths when imaging high albedo targets at low attenuation lengths, and we achieve a depth resolution (σ) ranging from 0.8 to 9 cm, depending on signal level.

Active illumination 3D imaging systems based on Time-of-flight (TOF) and Structured Light (SL) projection are in rapid development, and are constantly finding new areas of application. In this paper, we present a theoretical design tool that allows prediction of 3D imaging precision. Theoretical expressions are developed for both TOF and SL imaging systems. The expressions contain only physically measurable parameters and no fitting parameters. We perform 3D measurements with both TOF and SL imaging systems, showing excellent agreement between theoretical and measured distance precision. The theoretical framework can be a powerful 3D imaging design tool, as it allows for prediction of 3D measurement precision already in the design phase.