Search Results

Now showing 1 - 10 of 23
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    Example-based Interpolation and Synthesis of Bidirectional Texture Functions
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Ruiters, Roland; Schwartz, Christopher; Klein, Reinhard; I. Navazo, P. Poulin
    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.
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    Data Driven Surface Reflectance from Sparse and Irregular Samples
    (The Eurographics Association and John Wiley and Sons Ltd., 2012) Ruiters, Roland; Schwartz, Christopher; Klein, Reinhard; P. Cignoni and T. Ertl
    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.
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    Heightfield and spatially varying BRDF Reconstruction for Materials with Interreflections
    (The Eurographics Association and Blackwell Publishing Ltd, 2009) Ruiters, Roland; Klein, Reinhard
    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.
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    Temporal Upsampling of Point Cloud Sequences by Optimal Transport for Plant Growth Visualization
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Golla, Tim; Kneiphof, Tom; Kuhlmann, Heiner; Weinmann, Michael; Klein, Reinhard; Benes, Bedrich and Hauser, Helwig
    Plant growth visualization from a series of 3D scanner measurements is a challenging task. Time intervals between successive measurements are typically too large to allow a smooth animation of the growth process. Therefore, obtaining a smooth animation of the plant growth process requires a temporal upsampling of the point cloud sequence in order to obtain approximations of the intermediate states between successive measurements. Additionally, there are suddenly arising structural changes due to the occurrence of new plant parts such as new branches or leaves. We present a novel method that addresses these challenges via semantic segmentation and the generation of a segment hierarchy per scan, the matching of the hierarchical representations of successive scans and the segment‐wise computation of optimal transport. The transport problems' solutions yield the information required for a realistic temporal upsampling, which is generated in real time. Thereby, our method does not require shape templates, good correspondences or huge databases of examples. Newly grown and decayed parts of the plant are detected as unmatched segments and are handled by identifying corresponding bifurcation points and introducing virtual segments in the previous, respectively successive time step. Our method allows the generation of realistic upsampled growth animations with moderate computational effort.
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    A Volumetric Approach to Predictive Rendering of Fabrics
    (The Eurographics Association and Blackwell Publishing Ltd., 2011) Schröder, Kai; Klein, Reinhard; Zinke, Arno; Ravi Ramamoorthi and Erik Reinhard
    Efficient physically accurate modeling and rendering of woven cloth at a yarn level is an inherently complicated task due to the underlying geometrical and optical complexity. In this paper, a novel and general approach to physically accurate cloth rendering is presented. By using a statistical volumetric model approximating the distribution of yarn fibers, a prohibitively costly explicit geometrical representation is avoided. As a result, accurate rendering of even large pieces of fabrics containing orders of magnitudes more fibers becomes practical without sacrifying much generality compared to fiber-based techniques. By employing the concept of local visibility and introducing the effective fiber density, limitations of existing volumetric approaches regarding self-shadowing and fiber density estimation are greatly reduced.
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    Data Preparation for Real-time High Quality Rendering of Complex Models
    (The Eurographics Association and Blackwell Publishing, Inc, 2006) Klein, Reinhard
    The capability of current 3D acquisition systems to digitize the geometry reflection behaviour of objects as well as the sophisticated application of CAD techniques lead to rapidly growing digital models which pose new challenges for interaction and visualization. Due to the sheer size of the geometry as well as the texture and reflection data which are often in the range of several gigabytes, efficient techniques for analyzing, compressing and rendering are needed. In this talk I will present some of the research we did in our graphics group over the past years motivated by industrial partners in order to automate the data preparation step and allow for real-time high quality rendering e.g. in the context of VR-applications. Strength and limitations of the different techniques will be discussed and future challenges will be identified. The presentation will go along with live demonstrations.
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    Learned Fitting of Spatially Varying BRDFs
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Merzbach, Sebastian; Hermann, Max; Rump, Martin; Klein, Reinhard; Boubekeur, Tamy and Sen, Pradeep
    The use of spatially varying reflectance models (SVBRDF) is the state of the art in physically based rendering and the ultimate goal is to acquire them from real world samples. Recently several promising deep learning approaches have emerged that create such models from a few uncalibrated photos, after being trained on synthetic SVBRDF datasets. While the achieved results are already very impressive, the reconstruction accuracy that is achieved by these approaches is still far from that of specialized devices. On the other hand, fitting SVBRDF parameter maps to the gibabytes of calibrated HDR images per material acquired by state of the art high quality material scanners takes on the order of several hours for realistic spatial resolutions. In this paper, we present a first deep learning approach that is capable of producing SVBRDF parameter maps more than two orders of magnitude faster than state of the art approaches, while still providing results of equal quality and generalizing to new materials unseen during the training. This is made possible by training our network on a large-scale database of material scans that we have gathered with a commercially available SVBRDF scanner. In particular, we train a convolutional neural network to map calibrated input images to the 13 parameter maps of an anisotropic Ward BRDF, modified to account for Fresnel reflections, and evaluate the results by comparing the measured images against re-renderings from our SVBRDF predictions. The novel approach is extensively validated on real world data taken from our material database, which we make publicly available under https://cg.cs.uni-bonn.de/svbrdfs/.
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    Photo-realistic Rendering of Metallic Car Paint from Image-Based Measurements
    (The Eurographics Association and Blackwell Publishing Ltd, 2008) Rump, Martin; Mueller, Gero; Sarlette, Ralf; Koch, Dirk; Klein, Reinhard
    State-of-the-art car paint shows not only interesting and subtle angular dependency but also significant spatial variation. Especially in sunlight these variations remain visible even for distances up to a few meters and give the coating a strong impression of depth which cannot be reproduced by a single BRDF model and the kind of procedural noise textures typically used. Instead of explicitly modeling the responsible effect particles we propose to use image-based reflectance measurements of real paint samples and represent their spatial varying part by Bidirectional Texture Functions (BTF). We use classical BRDF models like Cook-Torrance to represent the reflection behavior of the base paint and the highly specular finish and demonstrate how the parameters of these models can be derived from the BTF measurements. For rendering, the image-based spatially varying part is compressed and efficiently synthesized. This paper introduces the first hybrid analytical and image-based representation for car paint and enables the photo-realistic rendering of all significant effects of highly complex coatings.
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    Completion and Reconstruction with Primitive Shapes
    (The Eurographics Association and Blackwell Publishing Ltd, 2009) Schnabel, Ruwen; Degener, Patrick; Klein, Reinhard
    We consider the problem of reconstruction from incomplete point-clouds. To find a closed mesh the reconstruction is guided by a set of primitive shapes which has been detected on the input point-cloud (e.g. planes, cylinders etc.). With this guidance we not only continue the surrounding structure into the holes but also synthesize plausible edges and corners from the primitives intersections. To this end we give a surface energy functional that incorporates the primitive shapes in a guiding vector field. The discretized functional can be minimized with an efficient graph-cut algorithm. A novel greedy optimization strategy is proposed to minimize the functional under the constraint that surface parts corresponding to a given primitive must be connected. From the primitive shapes our method can also reconstruct an idealized model that is suitable for use in a CAD system.
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    Level-of-Detail Streaming and Rendering using Bidirectional Sparse Virtual Texture Functions
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Schwartz, Christopher; Ruiters, Roland; Klein, Reinhard; B. Levy, X. Tong, and K. Yin
    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.