Redefining A in RGBA: Towards a Standard for Graphical 3D Printing

Advances in multimaterial 3D printing have the potential to reproduce various visual appearance attributes of an object in addition to its shape. Since many existing 3D file formats encode color and translucency by RGBA textures mapped to 3D shapes, RGBA information is particularly important for practical applications. In contrast to color (encoded by RGB), which is specified by the object's reflectance, selected viewing conditions and a standard observer, translucency (encoded by A) is neither linked to any measurable physical nor perceptual quantity. Thus, reproducing translucency encoded by A is open for interpretation. In this paper, we propose a rigorous definition for A suitable for use in graphical 3D printing, which is independent of the 3D printing hardware and software, and which links both optical material properties and perceptual uniformity for human observers. By deriving our definition from the absorption and scattering coefficients of virtual homogeneous reference materials with an isotropic phase function, we achieve two important properties. First, a simple adjustment of A is possible, which preserves the translucency appearance if an object is re-scaled for printing. Second, determining the value of A for a real (potentially non-homogeneous) material, can be achieved by minimizing a distance function between light transport measurements of this material and simulated measurements of the reference materials. Such measurements can be conducted by commercial spectrophotometers used in graphic arts. Finally, we conduct visual experiments employing the method of constant stimuli, and derive from them an embedding of A into a nearly perceptually uniform scale of translucency for the reference materials.Comment: 20 pages (incl. appendices), 20 figures. Version with higher quality images: https://cloud-ext.igd.fraunhofer.de/s/pAMH67XjstaNcrF (main article) and https://cloud-ext.igd.fraunhofer.de/s/4rR5bH3FMfNsS5q (appendix). Supplemental material including code: https://cloud-ext.igd.fraunhofer.de/s/9BrZaj5Uh5d0cOU/downloa

The Impact of Surface Normals on Appearance

The appearance of an object is the result of complex light interaction with the object. Beyond the basic interplay between incident light and the object\u27s material, a multitude of physical events occur between this illumination and the microgeometry at the point of incidence, and also beneath the surface. A given object, made as smooth and opaque as possible, will have a completely different appearance if either one of these attributes - amount of surface mesostructure (small-scale surface orientation) or translucency - is altered. Indeed, while they are not always readily perceptible, the small-scale features of an object are as important to its appearance as its material properties. Moreover, surface mesostructure and translucency are inextricably linked in an overall effect on appearance. In this dissertation, we present several studies examining the importance of surface mesostructure (small-scale surface orientation) and translucency on an object\u27s appearance. First, we present an empirical study that establishes how poorly a mesostructure estimation technique can perform when translucent objects are used as input. We investigate the two major factors in determining an object\u27s translucency: mean free path and scattering albedo. We exhaustively vary the settings of these parameters within realistic bounds, examining the subsequent blurring effect on the output of a common shape estimation technique, photometric stereo. Based on our findings, we identify a dramatic effect that the input of a translucent material has on the quality of the resultant estimated mesostructure. In the next project, we discuss an optimization technique for both refining estimated surface orientation of translucent objects and determining the reflectance characteristics of the underlying material. For a globally planar object, we use simulation and real measurements to show that the blurring effect on normals that was observed in the previous study can be recovered. The key to this is the observation that the normalization factor for recovered normals is proportional to the error on the accuracy of the blur kernel created from estimated translucency parameters. Finally, we frame the study of the impact of surface normals in a practical, image-based context. We discuss our low-overhead, editing tool for natural images that enables the user to edit surface mesostructure while the system automatically updates the appearance in the natural image. Because a single photograph captures an instant of the incredibly complex interaction of light and an object, there is a wealth of information to extract from a photograph. Given a photograph of an object in natural lighting, we allow mesostructure edits and infer any missing reflectance information in a realistically plausible way

AirCode: Unobtrusive Physical Tags for Digital Fabrication

We present AirCode, a technique that allows the user to tag physically fabricated objects with given information. An AirCode tag consists of a group of carefully designed air pockets placed beneath the object surface. These air pockets are easily produced during the fabrication process of the object, without any additional material or postprocessing. Meanwhile, the air pockets affect only the scattering light transport under the surface, and thus are hard to notice to our naked eyes. But, by using a computational imaging method, the tags become detectable. We present a tool that automates the design of air pockets for the user to encode information. AirCode system also allows the user to retrieve the information from captured images via a robust decoding algorithm. We demonstrate our tagging technique with applications for metadata embedding, robotic grasping, as well as conveying object affordances.Comment: ACM UIST 2017 Technical Paper

Separable Subsurface Scattering

In this paper, we propose two real-time models for simulating subsurface scattering for a large variety of translucent materials, which need under 0.5 ms per frame to execute. This makes them a practical option for real-time production scenarios. Current state-of-the-art, real-time approaches simulate subsurface light transport by approximating the radially symmetric non-separable diffusion kernel with a sum of separable Gaussians, which requires multiple (up to 12) 1D convolutions. In this work we relax the requirement of radial symmetry to approximate a 2D diffuse reflectance profile by a single separable kernel. We first show that low-rank approximations based on matrix factorization outperform previous approaches, but they still need several passes to get good results. To solve this, we present two different separable models: the first one yields a high-quality diffusion simulation, while the second one offers an attractive trade-off between physical accuracy and artistic control. Both allow rendering of subsurface scattering using only two 1D convolutions, reducing both execution time and memory consumption, while delivering results comparable to techniques with higher cost. Using our importance-sampling and jittering strategies, only seven samples per pixel are required. Our methods can be implemented as simple post-processing steps without intrusive changes to existing rendering pipelines

BSSRDF estimation from single images

We present a novel method to estimate an approximation of the reflectance characteristics of optically thick, homogeneous translucent materials using only a single photograph as input. First, we approximate the diffusion profile as a linear combination of piecewise constant functions, an approach that enables a linear system minimization and maximizes robustness in the presence of suboptimal input data inferred from the image. We then fit to a smoother monotonically decreasing model, ensuring continuity on its first derivative. We show the feasibility of our approach and validate it in controlled environments, comparing well against physical measurements from previous works. Next, we explore the performance of our method in uncontrolled scenarios, where neither lighting nor geometry are known. We show that these can be roughly approximated from the corresponding image by making two simple assumptions: that the object is lit by a distant light source and that it is globally convex, allowing us to capture the visual appearance of the photographed material. Compared with previous works, our technique offers an attractive balance between visual accuracy and ease of use, allowing its use in a wide range of scenarios including off-the-shelf, single images, thus extending the current repertoire of real-world data acquisition techniques

New 3D scanning techniques for complex scenes

This thesis presents new 3D scanning methods for complex scenes, such as surfaces with fine-scale geometric details, translucent objects, low-albedo objects, glossy objects, scenes with interreflection, and discontinuous scenes. Starting from the observation that specular reflection is a reliable visual cue for surface mesostructure perception, we propose a progressive acquisition system that captures a dense specularity field as the only information for mesostructure reconstruction. Our method can efficiently recover surfaces with fine-scale geometric details from complex real-world objects. Translucent objects pose a difficult problem for traditional optical-based 3D scanning techniques. We analyze and compare two descattering methods, phaseshifting and polarization, and further present several phase-shifting and polarization based methods for high quality 3D scanning of translucent objects. We introduce the concept of modulation based separation, where a high frequency signal is multiplied on top of another signal. The modulated signal inherits the separation properties of the high frequency signal and allows us to remove artifacts due to global illumination. Thismethod can be used for efficient 3D scanning of scenes with significant subsurface scattering and interreflections.Diese Dissertation präsentiert neuartige Verfahren für die 3D-Digitalisierung komplexer Szenen, wie z.B. Oberflächen mit sehr feinen Strukturen, durchscheinende Objekte, Gegenstände mit geringem Albedo, glänzende Objekte, Szenen mit Lichtinterreflektionen und unzusammenhängende Szenen. Ausgehend von der Beobachtung, daß die spekulare Reflektion ein zuverlässiger, visueller Hinweis für die Mesostruktur einer Oberfläche ist, stellen wir ein progressives Meßsystem vor, um Spekularitätsfelder zu messen. Aus diesen Feldern kann anschließend die Mesostruktur rekonstruiert werden. Mit unserer Methode können Oberflächen mit sehr feinen Strukturen von komplexen, realen Objekten effizient aufgenommen werden. Durchscheinende Objekte stellen ein großes Problem für traditionelle, optischbasierte 3D-Rekonstruktionsmethoden dar. Wir analysieren und vergleichen zwei verschiedene Methoden zum Eliminieren von Lichtstreuung (Descattering): Phasenverschiebung und Polarisation. Weiterhin präsentieren wir mehrere hochqualitative 3D-Rekonstruktionsmethoden für durchscheinende Objekte, die auf Phasenverschiebung und Polarisation basieren. Außerdem führen wir das Konzept der modulationsbasierten Signaltrennung ein. Hierzu wird ein hochfrequentes Signal zu einem anderes Signal multipliziert. Das so modulierte Signal erhält damit die separierenden Eigenschaften des hochfrequenten Signals. Dies erlaubt unsMeßartefakte aufgrund von globalen Beleuchtungseffekten zu vermeiden. Dieses Verfahren kann zum effizienten 3DScannen von Szenen mit durchscheinden Objekten und Interreflektionen benutzt werden

Material Recognition Meets 3D Reconstruction : Novel Tools for Efficient, Automatic Acquisition Systems

For decades, the accurate acquisition of geometry and reflectance properties has represented one of the major objectives in computer vision and computer graphics with many applications in industry, entertainment and cultural heritage. Reproducing even the finest details of surface geometry and surface reflectance has become a ubiquitous prerequisite in visual prototyping, advertisement or digital preservation of objects. However, today's acquisition methods are typically designed for only a rather small range of material types. Furthermore, there is still a lack of accurate reconstruction methods for objects with a more complex surface reflectance behavior beyond diffuse reflectance. In addition to accurate acquisition techniques, the demand for creating large quantities of digital contents also pushes the focus towards fully automatic and highly efficient solutions that allow for masses of objects to be acquired as fast as possible. This thesis is dedicated to the investigation of basic components that allow an efficient, automatic acquisition process. We argue that such an efficient, automatic acquisition can be realized when material recognition "meets" 3D reconstruction and we will demonstrate that reliably recognizing the materials of the considered object allows a more efficient geometry acquisition. Therefore, the main objectives of this thesis are given by the development of novel, robust geometry acquisition techniques for surface materials beyond diffuse surface reflectance, and the development of novel, robust techniques for material recognition. In the context of 3D geometry acquisition, we introduce an improvement of structured light systems, which are capable of robustly acquiring objects ranging from diffuse surface reflectance to even specular surface reflectance with a sufficient diffuse component. We demonstrate that the resolution of the reconstruction can be increased significantly for multi-camera, multi-projector structured light systems by using overlappings of patterns that have been projected under different projector poses. As the reconstructions obtained by applying such triangulation-based techniques still contain high-frequency noise due to inaccurately localized correspondences established for images acquired under different viewpoints, we furthermore introduce a novel geometry acquisition technique that complements the structured light system with additional photometric normals and results in significantly more accurate reconstructions. In addition, we also present a novel method to acquire the 3D shape of mirroring objects with complex surface geometry. The aforementioned investigations on 3D reconstruction are accompanied by the development of novel tools for reliable material recognition which can be used in an initial step to recognize the present surface materials and, hence, to efficiently select the subsequently applied appropriate acquisition techniques based on these classified materials. In the scope of this thesis, we therefore focus on material recognition for scenarios with controlled illumination as given in lab environments as well as scenarios with natural illumination that are given in photographs of typical daily life scenes. Finally, based on the techniques developed in this thesis, we provide novel concepts towards efficient, automatic acquisition systems