Structured-light stereo: Comparative analysis and integration of structured-light and active stereo for measuring dynamic shape

Abstract In computer vision, two major active range imaging methods have been frequently employed for rapid and efficient shape recovery: (a) conventional active stereo vision and (b) conventional structured-light vision. This paper presents a comparative analysis and an integration of the two active approaches, namely, a structured-light stereo approach for the acquisition of dynamic shape. We first investigate the strengths and weaknesses of the two approaches in terms of accuracy, computational cost, field of view, depth of field, and color sensitivity. Based on this analysis, we propose a novel integrated method, the structured-light stereo, to recover dynamic shapes from a wider view with less occlusion by taking most of the benefits of the two approaches. The main idea is as follows. We first build a system composed of two cameras and a single projector (just a basic setup for conventional active stereo), and the projector projects a single “one-shot” color-stripe pattern. The next step is to estimate reliable correspondences between each camera and the projector via an accurate and efficient pattern decoding technique, and some remaining unresolved regions are explored by a stereo matching technique, which is less sensitive to object surface colors and defocus due to the projector's short depth of field, to estimate additional correspondences. We demonstrate the efficacy of the integrated method through experimental results.