Structured light (SL) has a trade-off between acquisition time and spatial resolution. Temporally coded SL can produce a 3D reconstruction with high density, yet it is not applicable to dynamic reconstruction. On the contrary, spatially coded SL works with a single shot, but it can only achieve sparse reconstruction. This paper aims to achieve accurate 3D dense and dynamic reconstruction at the same time. A speckle-based SL sensor is presented, which consists of two cameras and a diffractive optical element (DOE) projector. The two cameras record images synchronously. First, a speckle pattern was elaborately designed and projected. Second, a high-accuracy calibration method was proposed to calibrate the system; meanwhile, the stereo images were accurately aligned by developing an optimized epipolar rectification algorithm. Then, an improved semi-global matching (SGM) algorithm was proposed to improve the correctness of the stereo matching, through which a high-quality depth map was achieved. Finally, dense point clouds could be recovered from the depth map. The DOE projector was designed with a size of 8 mm × 8 mm. The baseline between stereo cameras was controlled to be below 50 mm. Experimental results validated the effectiveness of the proposed algorithm. Compared with some other single-shot 3D systems, our system displayed a better performance. At close range, such as 0.4 m, our system could achieve submillimeter accuracy.

The reflection phenomenon exhibited by highly reflective surfaces considerably affects the quality of captured images, thereby rendering the task of structured light (SL) 3D reconstruction. In this paper, a polarized SL method is proposed to address the reconstruction issues on high-reflectance surfaces. The SL system we build in this paper involves a four-channel polarizing camera and a digital light processing (DLP) projector equipped with a polarizer in the lens. The built system enables the simultaneous acquisition of four groups of fringe images, each with different brightness differences. Then, a binary time-multiplexing SL method is adopted to obtain four distinct point clouds. Additionally, a fusion algorithm is proposed to merge the four point clouds into a single, precise, and complete point cloud. Several experiments have been conducted to demonstrate that the proposed method is capable of achieving excellent reconstruction outcomes on highly reflective surfaces.

High dynamic range object has large surface reflectivity variations, which easily produces saturated and low-contrast regions in the captured images, further resulting in large calculation errors of gray value or phase value, then seriously affecting the reconstruction accuracy. An auto-exposure-based structured light technique is proposed. In this method, the relationship between noise-induced phase error and intensity modulation is first analyzed. It is demonstrated that once the intensity modulation of a pixel is larger than a threshold, its phase error can be considered acceptable. Then, the surface reflectivity of the measured target is estimated by projecting a series of uniform gray-level patterns and classified into several groups. The exposure time for each group is automatically determined after establishing its mathematical model based on the modulation threshold. Phase-shifted images are then captured at the computed exposure times, and a set of composite phase-shifted images is acquired by extracting the brightest unsaturated pixels in the raw fringe images. The experiments show that the proposed method can automatically calculate the exposure times and capture the fringe images without human intervention. In addition, it can well tackle saturated and dark areas of the measured target and the reconstructed three-dimensional shape is complete and accurate.