Poor surface color reproduction and incomplete color management system are the main impeding factors for the commercialization of full-color 3D printing. In this paper, the coloration mechanisms as well as characteristics of 3D surfaces were introduced, and a variety of impregnation methods suitable for powder-based 3D printing were integrated. The 24-color cards and four-primary cubes were printed by 3D Systems ProJet 860 Pro printer to compare single-plane and multi-plane optimization effects, choose the best impregnation process and put forward a guide to improve impregnants. The results revealed that the saturation of 3D printing surface color was greatly increased and the brightness was slightly decreased after impregnation process, which reduced chromatic aberration on single-plane or multi-plane. ColorBond and transparent coating spray are the most suitable combination for powder-based 3D objects. Increasing the uniformity, transparency and permeability of coatings is beneficial to further optimize surface colors.
3D shape reconstruction is one of the most important topics in computer vision and the foundation for a wide field of application. Among various technologies, structured light is one of the most reliable techniques. However, given the field of view of projectors and cameras available in the market, the working distance needed for projectors is typically larger than that for cameras. To reduce the working distance of the projectors while covering the whole working platform, two projectors with their field of view overlapping are used to cover the working area which holds objects to be scanned. We present a spectral analysis based model for the projector-camera system, in order to find the most distinguishable colors for two projectors, and best separate the projected patterns from two projectors. The optimal values of the two colors are determined by the pattern search method in the presence of noise, which is modeled as multivariate Gaussian noise, and characterized for different input colors. The camera sensors' responses to the projector are measured after linearization with gray balance curves. After being properly calibrated, based on one image shot of the object with binary M-array patterns projected on it, the system can reconstruct a 3D shape of the object surface.