Abstract. Single photon lidars (in solid state form) offer several benefits over pulsed lidars, such as independence of micro-mechanical moving parts or rotating joints, lower power consumption, faster acquisition rate, and reduced size. When mass produced, they will be cheaper and smaller and thus very attractive for mobile laser scanning applications. However, as these lidars operate by receiving single photons, they are very susceptible to background illumination such as sunlight. In other words, the observations contain a significant amount of noise, or to be specific, outliers. This causes trouble for measurements done in motion, as the sampling rate (i.e. the measurement frequency) should be low and high at the same time. It should be low enough so that target detection is robust, meaning that the targets can be distinguished from the single-photon avalanche diode (SPAD) triggings caused by the background photons. On the other hand, the sampling rate should be high enough to allow for measurements to be done from motion. Quick sampling reduces the probability that a sample gathered during motion would contain data from more than a single target at a specific range. Here, we study the exploitation of spatial correlations that exist between the observations as a mean to overcome this sampling rate paradox. We propose computational methods for short and long range. Our results indicate that the spatial correlations do indeed allow for faster and more robust sampling of measurements, which makes single photon lidars more attractive in (daylight) mobile laser scanning.
Learning Non-Local Spatial Correlations To Restore Sparse 3D Single-Photon Data