Force-displacement Force histogramPhysically based simulation Stiff Soft Figure 1: 3D printing allows us to print objects with varying deformation properties. The question that we want to answer is: Given a set of printing materials and a 3D object with desired elasticity properties, which material should be used to print the object? For example, given sample ducks (left) with desired elasticity properties (e.g., measured), our system considers several candidate materials that can be used for replicating the ducks (right), and chooses materials that will best match compliance properties when examined by an observer (red and green outlines). Moreover, we can sort all possible materials by their perceived compliance as predicted by our model. The measured compliance is indicated with colors ranging from stiff (blue) to soft (red). AbstractEveryone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments.

Material appearance hinges on material reflectance properties but also surface geometry and illumination. The unlimited number of potential combinations between these factors makes understanding and predicting material appearance a very challenging task. In this work, we collect a large-scale dataset of perceptual ratings of appearance attributes with more than 215,680 responses for 42,120 distinct combinations of material, shape, and illumination. The goal of this dataset is twofold. First, we analyze for the first time the effects of illumination and geometry in material perception across such a large collection of varied appearances. We connect our findings to those of the literature, discussing how previous knowledge generalizes across very diverse materials, shapes, and illuminations. Second, we use the collected dataset to train a deep learning architecture for predicting perceptual attributes that correlate with human judgments. We demonstrate the consistent and robust behavior of our predictor in various challenging scenarios, which, for the first time, enables estimating perceived material attributes from general 2D images. Since our predictor relies on the final appearance in an image, it can compare appearance properties across different geometries and illumination conditions. Finally, we demonstrate several applications that use our predictor, including appearance reproduction using 3D printing, BRDF editing by integrating our predictor in a differentiable renderer, illumination design, or material recommendations for scene design.

Digital drawing is becoming a favorite technique for many artists. It allows for quick swaps between different materials, reverting changes, and applying selective modifications to finished artwork. These features enable artists to be more efficient and creative. A significant disadvantage of digital drawing is poor haptic feedback. Artists are usually limited to one surface and a few different stylus nibs, and while they try to find a combination that suits their needs, this is typically challenging. In this work, we address this problem and propose a method for designing, evaluating, and optimizing different stylus designs. We begin with collecting a representative set of traditional drawing tools. We measure their physical properties and conduct a user experiment to build a perceptual space that encodes perceptually-relevant attributes of drawing materials. The space is optimized to both explain our experimental data and correlate it with measurable physical properties. To embed new drawing tool designs into the space without conducting additional experiments and measurements, we propose a new, data-driven simulation technique for characterizing stylus-surface interaction. We finally leverage the perceptual space, our simulation, and recent advancements in multi-material 3D printing to demonstrate the application of our system in the design of new digital drawing tools that mimic traditional drawing materials.

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