  Single-photon light detection and ranging (lidar) captures depth and
intensity information of a 3D scene. Reconstructing a scene from observed
photons is a challenging task due to spurious detections associated with
background illumination sources. To tackle this problem, there is a plethora of
3D reconstruction algorithms which exploit spatial regularity of natural scenes
to provide stable reconstructions. However, most existing algorithms have
computational and memory complexity proportional to the number of recorded
photons. This complexity hinders their real-time deployment on modern lidar
arrays which acquire billions of photons per second. Leveraging a recent lidar
sketching framework, we show that it is possible to modify existing
reconstruction algorithms such that they only require a small sketch of the
photon information. In particular, we propose a sketched version of a recent
state-of-the-art algorithm which uses point cloud denoisers to provide
spatially regularized reconstructions. A series of experiments performed on
real lidar datasets demonstrates a significant reduction of execution time and
memory requirements, while achieving the same reconstruction performance than
in the full data case.
