Performance evaluation of time-of-flight and structured light depth sensors in radiometric/geometric variations

Abstract

Time-of-flight (ToF) and structured light depth cameras capture dense three-dimensional (3-D) geometry that is of great benefit for many computer vision problems. For the past couple of years, depth image based gesture recognition, 3-D reconstruction, and robot localization have received explosive interest in the literature. However, depth measurements present unique systematic errors, specifically when objects have specularity or translucency. We present a quantitative evaluation and analysis of depth errors using both ToF and structured light depth cameras. The evaluation framework used includes a dataset of carefully taken depth images with radiometric/geometric variations of real world objects and their ground truth depth. Our analysis and experiments reveal the different characteristics of the two sensor types and indicate that obtaining high quality depth image from real-world scene still remains a challenging, unsolved problem.