Learned Optimal Visual Time-of-Flight Imaging With Fisher Information Guidance

This paper presents a method to improve indirect time-of-flight (iToF) imaging, a technology that measures depth by analyzing how light bounces off objects. The researchers use machine learning to optimize how the imaging system encodes and decodes light signals, guided by Fisher information (a measure of how much useful information data contains), while also incorporating visual data from regular RGB cameras to make depth measurements more accurate, especially in noisy conditions.