Time-correlated single-photon-counting (TCSPC) lidar provides very high resolution range measurements. This makes the technology interesting for three-dimensional imaging of complex scenes with targets behind foliage or other obscurations. TCSPC is a statistical method that demands integration of multiple measurements toward the same area to resolve objects at different distances within the instantaneous field-of-view. Point-by-point scanning will demand significant overhead for the movement, increasing the measurement time. Here, the effect of continuously scanning the scene row-by-row is investigated and signal processing methods to transform this into low-noise point clouds are described. The methods are illustrated using measurements of a characterization target and an oak and hazel copse. Steps between different surfaces of less than 5 cm in range are resolved as two surfaces.