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Single-photon light detection and ranging (lidar) captures depth andintensity information of a 3D scene. Reconstructing a scene from observedphotons is a challenging task due to spurious detections associated withbackground illumination sources. To tackle this problem, there is a plethora of3D reconstruction algorithms which exploit spatial regularity of natural scenesto provide stable reconstructions. However, most existing algorithms havecomputational and memory complexity proportional to the number of recordedphotons. This complexity hinders their real-time deployment on modern lidararrays which acquire billions of photons per second. Leveraging a recent lidarsketching framework, we show that it is possible to modify existingreconstruction algorithms such that they only require a small sketch of thephoton information. In particular, we propose a sketched version of a recentstate-of-the-art algorithm which uses point cloud denoisers to providespatially regularized reconstructions. A series of experiments performed onreal lidar datasets demonstrates a significant reduction of execution time andmemory requirements, while achieving the same reconstruction performance thanin the full data case.

Sketched RT3D: How to reconstruct billions of photons per second