Structured Light Stereoscopic Imaging with Dynamic Pseudo-random Patterns

Structured light stereoscopic imaging offers an efficient and affordable solution to 3D modeling of objects. The majority of structured light patterns that have been proposed either provide a limited resolution or are sensitive to the inherent texture on the surface of the object. This paper proposes an innovative imaging strategy that accomplishes 3D reconstruction of objects using a combination of spatial-neighboring and time-multiplexing structured light patterns encoded with uniquely defined pseudo-random color codes. The approach is extended with the concept of dynamic patterns that adaptively increases the reconstruction resolution. Original techniques are introduced to recover and validate pseudo-random codes from stereoscopic images, and to consistently map color and texture over the reconstructed surface map. Experimental results demonstrate the potential of the solution to create reconstructions with various densities of points and prove the robustness of the approach on objects with different surface properties.