An Experimental Assessment of Depth Estimation in Transparent and Translucent Scenes for Intel RealSense D415, SR305 and L515

Abstract

RGB-D cameras have become common in many research fields since these inexpensive devices provide dense 3D information from the observed scene. Over the past few years, the RealSense™ range from Intel® has introduced new, cost-effective RGB-D sensors with different technologies, more sophisticated in both hardware and software. Models D415, SR305, and L515 are examples of successful cameras launched by Intel® RealSense™ between 2018 and 2020. These three cameras are different since they have distinct operating principles. Then, their behavior concerning depth estimation while in the presence of many error sources will also be specific. For instance, semi-transparent and scattering media are expected error sources for an RGB-D sensor. The main new contribution of this paper is a full evaluation and comparison between the three Intel RealSense cameras in scenarios with transparency and translucency. We propose an experimental setup involving an aquarium and liquids. The evaluation, based on repeatability/precision and statistical distribution of the acquired depth, allows us to compare the three cameras and conclude that Intel RealSense D415 has overall the best behavior namely in what concerns the statistical variability (also known as precision or repeatability) and also in what concerns valid measurements.