There is a growing need for high-accuracy, low-cost 3D scanning processes that can tolerate challenging conditions such as relative motion between scanner and scanned object, non-Lambertian materials and a variety of lighting conditions.
Structured Light (SL) techniques are the best current methods for accurate capture of 3 dimensional shapes. These are active techniques that illuminate objects or environments of interest with specially designed patterns of visible or invisible light. Images of the objects and/or environments are then captured with one or more cameras while the special patterns are illuminating the objects and/or environments. The 3D geometry is calculated from the images with triangulation using knowledge of relative angle, displacement and optical factors for the camera and projector. The active light source allows results to be relatively invariant to different material and environmental properties such as color, texture and ambient illumination. Modern light projection engines, image sensors and digital signal processing (DSP) device technology can project and capture high resolution images at high frame rate reliably and accurately.
The significant qualities of the results of structured light techniques are determined by the characteristics of the patterns (and usually the temporal sequence of patterns) that are projected onto the object or environment to be captured. The purpose of the patterns is to encode information that enables camera image coordinates to be directly related to projected image coordinates. Projected patterns typically encode the projector image column or row coordinates so that with the use of optical and geometric calibration information, it becomes possible to use optical triangulation to identify 3 dimensional (3D) space coordinates of the object being scanned which correspond to each pixel in the projector coordinate space or sometimes each pixel in the captured camera images.
Structured light patterns are typically classified according to whether they allow retrieval of 3D coordinates corresponding to discrete projector pixel locations or whether they allow sub-pixel (i.e. continuous) measurements. Continuous patterns may be able to find a different 3D coordinate for each camera pixel coordinate, or even camera sub-pixel coordinates, whereas, discrete patterns only identify positions corresponding to discrete projector pixel coordinates. Results from discrete techniques may only have as many 3D points as projector pixels, whereas 3D models resulting from conventional continuous techniques may have as many 3D points as camera pixels. See, e.g., D. Moreno, W. Y. Hwang and G. Taubin. Rapid Hand Shape Reconstruction with Chebyshev Phase Shifting. 2016 Fourth International Conference on 3D Vision. Results from advanced techniques presented here may have camera sub-pixel resolution meaning that they may have more 3D points than camera pixels.
Conventionally, continuous techniques require better control of projected colors and intensities as well as camera to projector color and intensity correspondence and calibration of colors and intensities is necessary. In contrast, discrete techniques may not require this level of control and calibration with the downside that they may be slower and yield lower resolution.
Many continuous techniques, generally known as Phase Shifting (PS) encode a projector axis (typically the X axis of the projected image) as sinusoidal grayscale or color patterns. PS techniques are more tolerant of projector defocus which is unavoidable when using large optical apertures typical in digital projectors.
Current PS 3D scanning techniques require capturing multiple images of an object or scene per static data set and generally make the assumption in their algorithms that the images are of the same scene from the same vantage point. Therefore they have a requirement of little relative motion between scanner and object or environment during the entire multiple-image capture duration for acquisition of each individual dataset. To a certain extent the limitations of relative motion can be overcome using higher and higher frame rates, but there are direct advantages to be had in 3 dimensional accuracy, data quality and quantity and color accuracy and mapping accuracy if the number of images to be captured per data set can be reduced, and especially reducing the required number of images to be captured under the influence of non-uniform illumination patterns.