Pattern codification strategies in structured light systems

Abstract

Coded structured light is considered one of the most reliable techniques for recovering the surface of objects. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. The decoded points can be triangulated and 3D information is obtained. We present an overview of the existing techniques, as well as a new and deÿnitive classiÿcation of patterns for structured light sensors. We have implemented a set of representative techniques in this ÿeld and present some comparative results. The advantages and constraints of the di erent patterns are also discussed.

Frequency-division-multiplexing based period-coded fringe pattern for reliable depth sensing

“…The De Bruijn sequence is suitable for encoding patterns [32]. A De Bruijn sequence of order m over an alphabet of n symbols is a circular string of length n m .…”

“…This pseudorandom sequence has been used to encode grid-based pattern [33] in the colorspace. However, the number of colors used increases the noise sensitivity when measuring colourful scenes [32].…”

Accurate stereo matching using pixel normalized cross correlation in time domain

“…Therefore, detecting the same code on the camera pixels quickly solves the correspondence problem between projector and camera pixels for a large number of points and leads to dense 3D surface acquisition. Based on the concept of coding the image pixels, three main approaches in SL can be identified: the coding techniques in spatial and temporal domain [15], and there is also coding in the frequency domain called Fourier Transform Profilometry (FTP) [16]. SL spatial coding often uses color which is sensitive to object albedo and the leakage between color channels [17].…”

Reconstruction of point objects spatial coordinates from two-dimensional images

“…По-сле этого пространственные координаты точек рассчитываются известным образом по координатам их изображений. Выявление соответственных точек и определяет разнообразие методов определения про-странственных координат [6][7][8][9][10][11][12][13][14] объектов. Так, возможен корреляционный подход, когда соответствен-ные точки определяются по окружающим их фрагментам изображений [8,9].…”