Line structured light techniques, especially laser scanning, are preferred for commercialized three-dimensional (3-D) shape acquisition. Typically, a captured line stripe is to be detected spatially within a single image, and the detection may fail if there are ambiguities in the image. We present a decoding strategy for line structured light patterns. Over all recorded line-patterned images, by means of Fourier analysis along the time axis for each pixel, a phase map is computed and employed for 3-D reconstruction. The phase error is theoretically analyzed. Experimental results demonstrate that, compared with typical approaches based on spatial stripe peak detection, the proposed method achieves comparable accuracy and, most importantly, successfully handles the issue of ambiguities.
Measuring surfaces with high reflectivity variation via structured light illumination requires accurately identifying saturated pixels in captured images. However, conventional methods simply determine saturation by intensities, which is susceptible to a camera blurring effect and random noise. To solve this problem, we present a method that uses the magnitude of a nonprincipal frequency component to identify saturated pixels. Experimental results demonstrate that 1) higher accuracy of three-dimensional reconstruction can be achieved and 2) high-contrast surfaces can be accurately reconstructed.
In structured light illumination (SLI), the nonlinear distortion of the optical devices dramatically ruins accuracy of three-dimensional reconstruction when using only a small number of projected patterns. We propose a universal algorithm to calibrate these device nonlinearities to accurately precompensate the patterns. Thus, no postprocessing is needed to correct for the distortions while the number of patterns can be reduced down to as few as possible. Theoretically, the proposed method can be applied to any SLI pattern strategy. Using a three-pattern SLI method, our experimental results will show a 25× to 60× reduction in surface variance for a flat target, depending upon any surface smoothing that might be applied to remove Gaussian noise.