In this paper, we present a novel, robust multi-view normal field integration technique for reconstructing the full 3D shape of mirroring objects. We employ a turntable-based setup with several cameras and displays. These are used to display illumination patterns which are reflected by the object surface. The pattern information observed in the cameras enables the calculation of individual volumetric normal fields for each combination of camera, display and turntable angle. As the pattern information might be blurred depending on the surface curvature or due to non-perfect mirroring surface characteristics, we locally adapt the decoding to the finest still resolvable pattern resolution. In complex real-world scenarios, the normal fields contain regions without observations due to occlusions and outliers due to interreflections and noise. Therefore, a robust reconstruction using only normal information is challenging. Via a non-parametric clustering of normal hypotheses derived for each point in the scene, we obtain both the most likely local surface normal and a local surface consistency estimate. This information is utilized in an iterative min-cut based variational approach to reconstruct the surface geometry.

Abstract Bidirectional Texture Functions (BTF) have proven to be a well‐suited representation for the reproduction of measured real‐world surface appearance and provide a high degree of realism. We present an approach for designing novel materials by interpolating between several measured BTFs. For this purpose, we transfer concepts from existing texture interpolation methods to the much more complex case of material interpolation. We employ a separation of the BTF into a heightmap and a parallax compensated BTF to cope with problems induced by parallax, masking and shadowing within the material. By working only on the factorized representation of the parallax compensated BTF and the heightmap, it is possible to efficiently perform the material interpolation. By this novel method to mix existing BTFs, we are able to design plausible and realistic intermediate materials for a large range of different opaque material classes. Furthermore, it allows for the synthesis of tileable and seamless BTFs and finally even the generation of gradually changing materials following user specified material distribution maps.

Abstract Bidirectional Texture Functions (BTFs) are among the highest quality material representations available today and thus well suited whenever an exact reproduction of the appearance of a material or complete object is required. In recent years, BTFs have started to find application in various industrial settings and there is also a growing interest in the cultural heritage domain. BTFs are usually measured from real‐world samples and easily consist of tens or hundreds of gigabytes. By using data‐driven compression schemes, such as matrix or tensor factorization, a more compact but still faithful representation can be derived. This way, BTFs can be employed for real‐time rendering of photo‐realistic materials on the GPU. However, scenes containing multiple BTFs or even single objects with high‐resolution BTFs easily exceed available GPU memory on today's consumer graphics cards unless quality is drastically reduced by the compression. In this paper, we propose the Bidirectional Sparse Virtual Texture Function, a hierarchical level‐of‐detail approach for the real‐time rendering of large BTFs that requires only a small amount of GPU memory. More importantly, for larger numbers or higher resolutions, the GPU and CPU memory demand grows only marginally and the GPU workload remains constant. For this, we extend the concept of sparse virtual textures by choosing an appropriate prioritization, finding a trade off between factorization components and spatial resolution. Besides GPU memory, the high demand on bandwidth poses a serious limitation for the deployment of conventional BTFs. We show that our proposed representation can be combined with an additional transmission compression and then be employed for streaming the BTF data to the GPU from from local storage media or over the Internet. In combination with the introduced prioritization this allows for the fast visualization of relevant content in the users field of view and a consecutive progressive refinement.

In this paper, we propose a 3D reconstruction approach which combines a structured light based consistency measure with dense normal information obtained by exploiting the Helmholtz reciprocity principle. This combination compensates for the individual limitations of techniques providing normal information, which are mainly affected by low-frequency drift, and those providing positional information, which are often not well-suited to recover fine details. To obtain Helmholtz reciprocal samples, we employ a turntable-based setup. Due to the reciprocity, the structured light directly provides the occlusion information needed during the normal estimation for both the cameras and light sources. We perform the reconstruction by solving one global variational problem which integrates all available measurements simultaneously, over all cameras, light source positions and turntable rotations. For this, we employ an octree-based continuous min-cut framework in order to alleviate metrification errors while maintaining memory efficiency. We evaluate the performance of our algorithm both on synthetic and real-world data.

Abstract In recent years, measuring surface reflectance has become an established method for high quality renderings. In this context, especially non‐parametric representations got a lot of attention as they allow for a very accurate representation of complex reflectance behavior. However, the acquisition of this data is a challenging task especially if complex object geometry is involved. Capturing images of the object under varying illumination and view conditions results in irregular angular samplings of the reflectance function with a limited angular resolution. Classical data‐driven techniques, like tensor factorization, are not well suited for such data sets as they require a resampling of the high dimensional measurement data to a regular grid. This grid has to be on a much higher angular resolution to avoid resampling artifacts which in turn would lead to data sets of enormous size. To overcome these problems we introduce a novel, compact data‐driven representation of reflectance functions based on a sum of separable functions which are fitted directly to the irregular set of data without any further resampling. The representation allows for efficient rendering and is also well suited for GPU applications. By exploiting spatial coherence of the reflectance function over the object a very precise reconstruction even of specular materials becomes possible already with a sparse input sampling. This would be impossible using standard data interpolation techniques. Since our algorithm exclusively operates on the compressed representation, it is both efficient in terms of memory use and computational complexity, depending only sub‐linearly on the size of the fully tabulated data. The quality of the reflectance function is evaluated on synthetic data sets as ground truth as well as on real world measurements.

Photo-realistic 3D content is crucial for creating convincing digital images. For certain classes of everyday objects, such as food, the human perception is sensitive to even small inconsistencies, making the creation of such content difficult and time-consuming, even for experts. In this sketch, we will explore the use of an automated pipeline for capturing 3D shape and Bidirectional Texture Function of food. The acquired data is used to render photo-realistic images of purely virtual objects under arbitrary lighting and with full global illumination.

Abstract In this paper, we present a novel exemplar‐based technique for the interpolation between two textures that combines patch‐based and statistical approaches. Motivated by the notion of texture as a largely local phenomenon, we warp and blend small image neighborhoods prior to patch‐based texture synthesis. In addition, interpolating and enforcing characteristic image statistics faithfully handles high frequency detail. We are able to create both intermediate textures as well as continuous transitions. In contrast to previous techniques computing a global morphing transformation on the entire input exemplar images, our localized and patch‐based approach allows us to successfully interpolate between textures with considerable differences in feature topology for which no smooth global warping field exists.

Abstract Photo‐realistic reproduction of material appearance from images has widespread use in applications ranging from movies over advertising to virtual prototyping. A common approach to this task is to reconstruct the small scale geometry of the sample and to capture the reflectance properties using spatially varying BRDFs. For this, multi‐view and photometric stereo reconstruction can be used, both of which are limited regarding the amount of either view or light directions and suffer from either low‐ or high‐frequency artifacts, respectively. In this paper, we propose a new algorithm combining both techniques to recover heightfields and spatially varying BRDFs while at the same time overcoming the above mentioned drawbacks. Our main contribution is a novel objective function which allows for the reconstruction of a heightfield and high quality SVBRDF including view dependent effects. Thereby, our method also avoids both low and high frequency artifacts. Additionally, our algorithm takes inter‐reflections into account allowing for the reconstruction of undisturbed representations of the underlying material. In our experiments, including synthetic and real‐world data, we show that our approach is superior to state‐of‐the‐art methods regarding reconstruction error as well as visual impression. Both the reconstructed geometry and the recovered SVBRDF are highly accurate, resulting in a faithful reproduction of the materials characteristic appearance, which is of paramount importance in the context of material rendering.

Abstract In this paper, we present a novel compression technique for Bidirectional Texture Functions based on a sparse tensor decomposition. We apply the K‐SVD algorithm along two different modes of a tensor to decompose it into a small dictionary and two sparse tensors. This representation is very compact, allowing for considerably better compression ratios at the same RMS error than possible with current compression techniques like PCA, N‐mode SVD and Per Cluster Factorization. In contrast to other tensor decomposition based techniques, the use of a sparse representation achieves a rendering performance that is at high compression ratios similar to PCA based methods.