Digital light processing stereolithography is a promising technique for 3D printing. However, it offers little control over the surface appearance of the printed object. The printing process is typically layered, which leads to aliasing artefacts that affect surface appearance. An antialiasing option is to use greyscale pixel values in the layer images that we supply to the printer. This enables a kind of subvoxel growth control. We explore this concept and use it for editing surface microstructure. In other words, we modify the surface appearance of a printed object by applying a greyscale pattern to the surface voxels before sending the cross‐sectional layer images to the printer. We find that a smooth noise function is an excellent tool for varying surface roughness and for breaking the regularities that lead to aliasing. Conversely, we also present examples that introduce regularities to produce controlled anisotropic surface appearance. Our hope is that subvoxel growth control in stereolithography can lead 3D printing towards customizable surface appearance. The printing process adds what we call ground noise to the printed result. We suggest a way of modelling this ground noise to provide users with a tool for estimating a printer's ability to control surface reflectance.

Engineering of surface structure to obtain specific anisotropic reflectance properties has interesting applications in large scale production of plastic items. In recent work, surface structure has been engineered to obtain visible reflectance contrast when observing a surface before and after rotating it 90 degrees around its normal axis. We build an analytic anisotropic reflectance model based on the microstructure engineered to obtain such contrast. Using our model to render synthetic images, we predict the above mentioned contrasts and compare our predictions with the measurements reported in previous work. The benefit of an analytical model like the one we provide is its potential to be used in computer vision for estimating the quality of a surface sample. The quality of a sample is indicated by the resemblance of camera-based contrast measurements with contrasts predicted for an idealized surface structure. Our predictive model is also useful in optimization of the microstructure configuration, where the objective for example could be to maximize reflectance contrast.

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We propose a method for direct comparison of rendered images with a corresponding photograph in order to analyze the optical properties of physical objects and test the appropriateness of appearance models. To this end, we provide a practical method for aligning a known object and a point-like light source with the configuration observed in a photograph. Our method is based on projective transformation of object edges and silhouette matching in the image plane. To improve the similarity between rendered and photographed objects, we introduce models for spatially varying roughness and a model where the distribution of light transmitted by a rough surface influences direction-dependent subsurface scattering. Our goal is to support development toward progressive refinement of appearance models through quantitative validation.

Figure 1: Pictures illustrating our VR demo application, with an in-game screenshot (left) and a picture of the setup (right).