Accurate color reproduction is important in many applications of 3D printing, from design prototypes to 3D color copies or portraits. Although full color is available via other technologies, multi-jet printers have greater potential for graphical 3D printing, in terms of reproducing complex appearance properties. However, to date these printers cannot produce full color, and doing so poses substantial technical challenges, from the shear amount of data to the translucency of the available color materials. In this article, we propose an error diffusion halftoning approach to achieve full color with multi-jet printers, which operates on multiple isosurfaces or layers within the object. We propose a novel traversal algorithm for voxel surfaces, which allows the transfer of existing error diffusion algorithms from 2D printing. The resulting prints faithfully reproduce colors, color gradients and fine-scale details.
A dithering algorithm is presented for application to local composition control (LCC) with three-dimensional printing (3D printing) to convert continuous-tone representation of objects with LCC into discrete (pointwise) version of machine instructions. The algorithm presented effectively reduces undesirable low frequency textures of composition for individual 3D layers and also for 3D volumes. Peculiarities of the 3D printing machine, including anisotropic geometry of its picture elements (PELs) and uncertainties in droplet placement, are addressed by adapting a standard digital halftoning algorithm. Without loss of generality, our algorithm also accounts for technical limitations in the printing device, only generating lattices that can be represented within the finite memory limits of the hardware.  We introduce a novel technique for generating green-noise halftones—stochastic dither patterns composed of homogeneously distributed pixel clusters. Although techniques employing error diffusion have been proposed previously, the technique here employs a dither array referred to as a green-noise mask, which greatly reduces the computational complexity formerly associated with green noise. Compared with those generated with blue-noise masks, halftones generated with green-noise masks are less susceptible to printer distortions. Because green noise constitutes patterns with widely varying cluster sizes and shapes, the technique introduced here for constructing these green-noise masks is tunable; that is, it allows for specific printer traits, with small clusters reserved for printers with low distortion and large clusters reserved for printers with high distortion. Given that blue noise is a limiting case of green noise, this new technique can even create blue-noise masks. A novel digital halftoning technique, by which the halftoning is achieved by a pixelwise comparison of the gray-scale image to an array (halftone screen), the blue-noise mask, is presented. This mask is designed so that the halftone image has blue-noise (high-frequency) characteristics in the frequency domain. The algorithm for the construction of the blue-noise mask and an algorithm for the construction of binary patterns with the same first-order but different second-order statistics are presented. Two psychovisual tests in which human subjects rated halftone patterns and images according to various criteria are also described.