Browsing by Author "Fellner, Dieter W."

Now showing 1 - 7 of 7
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    Automated Classification of Crests on Pottery Sherds Using Pattern Recognition on 2D Images
    (The Eurographics Association, 2022) Ritz, Martin; Santos, Pedro; Fellner, Dieter W.; Ponchio, Federico; Pintus, Ruggero
    Manual classification of artefacts is a labor intensive process. Based on 2D images and 3D scans of - for example - ceramic shards, we developed a pattern recognition algorithm which automatically extracts relief features for each newly recorded object and tries to automate the classification process. Based on characteristics found, previously unknown objects are automatically corelated to already classified objects of a collection exhibiting the greatest similarity. As a result, classes of artefacts form iteratively, which ultimately also corresponds to the overall goal which is the automated classification of entire collections. The greatest challenge in developing our software approach was the heterogeneity of reliefs, and in particular the fact that current machine learning approaches were out of question due to the very limited number of objects per class. This led to the implementation of an analytical approach that is capable of performing a classification based on very few artefacts.
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    End-to-end Color 3D Reproduction of Cultural Heritage Artifacts: Roseninsel Replicas
    (The Eurographics Association, 2019) Domajnko, Matevz; Tanksale, Tejas; Tausch, Reimar; Ritz, Martin; Knuth, Martin; Santos, Pedro; Fellner, Dieter W.; Rizvic, Selma and Rodriguez Echavarria, Karina
    Planning exhibitions of cultural artifacts is always challenging. Artifacts can be very sensitive to the environment and therefore their display can be risky. One way to circumvent this is to build replicas of these artifacts. Here, 3D digitization and reproduction, either physical via 3D printing or virtual, using computer graphics, can be the method of choice. For this use case we present a workflow, from photogrammetric acquisition in challenging environments to representation of the acquired 3D models in different ways, such as online visualization and color 3D printed replicas. This work can also be seen as a first step towards establishing a workflow for full color end-to-end reproduction of artifacts. Our workflow was applied on cultural artifacts found around the ''Roseninsel'' (Rose Island), an island in Lake Starnberg (Bavaria), in collaboration with the Bavarian State Archaeological Collection in Munich. We demonstrate the results of the end-to-end reproduction workflow leading to virtual replicas (online 3D visualization, virtual and augmented reality) and physical replicas (3D printed objects). In addition, we discuss potential optimizations and briefly present an improved state-of-the-art 3D digitization system for fully autonomous acquisition of geometry and colors of cultural heritage objects.
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    Joint Schedule and Layout Autotuning for Sparse Matrices with Compound Entries on GPUs
    (The Eurographics Association, 2019) Mueller-Roemer, Johannes Sebastian; Stork, AndrƩ; Fellner, Dieter W.; Schulz, Hans-Jƶrg and Teschner, Matthias and Wimmer, Michael
    Large sparse matrices with compound entries, i.e., complex and quaternionic matrices as well as matrices with dense blocks, are a core component of many algorithms in geometry processing, physically based animation, and other areas of computer graphics. We generalize several matrix layouts and apply joint schedule and layout autotuning to improve the performance of the sparse matrix-vector product on massively parallel graphics processing units. Compared to schedule tuning without layout tuning, we achieve speedups of up to 5.5x. In comparison to cuSPARSE, we achieve speedups of up to 4.7x
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    Lossless Compression of Multi-View Cultural Heritage Image Data
    (The Eurographics Association, 2019) von Buelow, Max; Guthe, Stefan; Ritz, Martin; Santos, Pedro; Fellner, Dieter W.; Rizvic, Selma and Rodriguez Echavarria, Karina
    Photometric multi-view 3D geometry reconstruction and material capture are important techniques for cultural heritage digitalization. Capturing images of artifacts with high resolution and high dynamic range and the possibility to store them losslessly enables future proof application of this data. As the images tend to consume immense amounts of storage, compression is essential for long time archiving. In this paper, we present a lossless image compression approach for multi-view and material reconstruction datasets with a strong focus on data created from cultural heritage digitalization. Our approach achieves compression rates of 2:1 compared against an uncompressed representation and 1.24:1 when compared against Gzip.
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    A Post Processing Technique to Automatically Remove Floater Artifacts in Neural Radiance Fields
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Wirth, Tristan; Rak, Arne; Knauthe, Volker; Fellner, Dieter W.; Chaine, Raphaƫlle; Deng, Zhigang; Kim, Min H.
    Neural Radiance Fields have revolutionized Novel View Synthesis by providing impressive levels of realism. However, in most in-the-wild scenes they suffer from floater artifacts that occur due to sparse input images or strong view-dependent effects. We propose an approach that uses neighborhood based clustering and a consistency metric on NeRF models trained on different scene scales to identify regions that contain floater artifacts based on Instant-NGPs multiscale occupancy grids. These occupancy grids contain the position of relevant optical densities in the scene. By pruning the regions that we identified as containing floater artifacts, they are omitted during the rendering process, leading to higher quality resulting images. Our approach has no negative runtime implications for the rendering process and does not require retraining of the underlying Multi Layer Perceptron. We show on a qualitative base, that our approach is suited to remove floater artifacts while preserving most of the scenes relevant geometry. Furthermore, we conduct a comparison to state-of-the-art techniques on the Nerfbusters dataset, that was created with measuring the implications of floater artifacts in mind. This comparison shows, that our method outperforms currently available techniques. Our approach does not require additional user input, but can be be used in an interactive manner. In general, the presented approach is applicable to every architecture that uses an explicit representation of a scene's occupancy distribution to accelerate the rendering process.
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    Profiling and Visualizing GPU Memory Access and Cache Behavior of Ray Tracers
    (The Eurographics Association, 2022) Buelow, Max von; Riemann, Kai; Guthe, Stefan; Fellner, Dieter W.; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
    Graphical processing units (GPUs) have gained popularity in recent years due to their efficiency in running massively parallel applications. Recent developments have also adapted ray-tracing algorithms to the GPU, where the bottleneck in the overall performance is usually given by the memory bandwidth. In this paper, we present an interactive, web-based visualization tool for GPU memory traces that provides visual insight into the memory and cache behavior of our reference ray tracer, by mapping internal GPU state back onto 3D objects. In order to visualize cache behavior, we use reuse distances on both GPU cache layers that are calculated on the basis of memory traces extracted from a real GPU using binary instrumentation. An advantage of our system is that it runs independently of the ray-tracing program. We further show visualizations of our GPU ray tracer and compare the visualizations of several ray-tracing approaches. We find our work to act as a convenient toolset to gather insights on which data structures and mesh regions can be cached efficiently, and how ray-tracing acceleration structures behave on various input meshes, bounding volume hierarchies, memory layouts, frame buffer resolutions, and work distribution techniques.
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    Segmentation-Based Near-Lossless Compression of Multi-View Cultural Heritage Image Data
    (The Eurographics Association, 2020) Buelow, Max von; Tausch, Reimar; Knauthe, Volker; Wirth, Tristan; Guthe, Stefan; Santos, Pedro; Fellner, Dieter W.; Spagnuolo, Michela and Melero, Francisco Javier
    Cultural heritage preservation using photometric approaches received increasing significance in the past years. Capturing of these datasets is usually done with high-end cameras at maximum image resolution enabling high quality reconstruction results while leading to immense storage consumptions. In order to maintain archives of these datasets, compression is mandatory for storing them at reasonable cost. In this paper, we make use of the mostly static background of the capturing environment that does not directly contribute information to 3d reconstruction algorithms and therefore may be approximated using lossy techniques. We use a superpixel and figure-ground segmentation based near-lossless image compression algorithm that transparently decides if regions are relevant for later photometric reconstructions. This makes sure that the actual artifact or structured background parts are compressed with lossless techniques. Our algorithm achieves compression rates compared to the PNG image compression standard ranging from 1:2 to 1:4 depending on the artifact size.