An openCL-based speckle matching on the monocular 3D sensor using speckle projection

Abstract

Single-shot speckle projection profilometry (SPP), which can build the global correspondences between stereo images by projecting a single random speckle pattern, is applicable to the dynamic 3D acquisition. However, the traditional stereo matching algorithm used in SPP has low matching accuracy and high computational cost, which makes it difficult to achieve real-time and accurate 3D reconstruction dynamically. For enhancing the performance of 3D sensing of single-shot speckle projection profilometry (SPP), in this paper, we proposed an OpenCL-based speckle matching on the monocular 3D sensor using speckle projection. In terms of hardware, our low-cost monocular 3D sensor using speckle projection only consists of one IR camera and a diffractive optical element (DOE) projector. On the other hand, an improved semi-global matching (SGM) algorithm using OpenCL acceleration was proposed to obtain efficient, dense, and accurate matching results, enabling high-quality 3D reconstruction dynamically. Since the baseline between the IR camera and the DOE projector is about 35mm, the absolute disparity range of our system is suitably set to 64 pixels to measure scenes with a depth range of 0:3m to 3m. The experiment results demonstrated that the proposed speckle matching method based on our low-cost 3D sensor can achieve fast and absolute 3D shape measurement with the millimeter accuracy through a single speckle pattern.