Raw Data Processing for Practical Time-of-Flight Super-Resolution

The relatively low resolution of Time-of-Flight (ToF) cameras, together with high power consumption and motion artifacts due to long exposure times, have kept ToF sensors away from classical lidar application fields, such as mobile robotics and autonomous driving. In this paper we note that while attempting to address the last two issues, e. g., via burst mode, the lateral resolution can be effectively increased. Differently from prior approaches, we propose a stripped-down modular super-resolution framework that operates in the raw data domain. Experimental validation using synthetic raw data from standard data sets and real raw data from a PMD ToF sensor confirmed the feasibility of the proposed pipeline.