Browsing by Author "Buelow, Max von"

Now showing 1 - 7 of 7
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    Alignment and Reassembly of Broken Specimens for Creep Ductility Measurements
    (The Eurographics Association, 2022) Knauthe, Volker; Kraus, Maurice; Buelow, Max von; Wirth, Tristan; Rak, Arne; Merth, Laurenz; Erbe, Alexander; Kontermann, Christian; Guthe, Stefan; Kuijper, Arjan; Fellner, Dieter W.; Bender, Jan; Botsch, Mario; Keim, Daniel A.
    Designing new types of heat-resistant steel components is an important and active research field in material science. It requires detailed knowledge of the inherent steel properties, especially concerning their creep ductility. Highly precise automatic stateof- the-art approaches for such measurements are very expensive and often times invasive. The alternative requires manual work from specialists and is time consuming and unrobust. In this paper, we present a novel approach that uses a photometric scanning system for capturing the geometry of steel specimens, making further measurement extractions possible. In our proposed system, we apply calibration for pan angles that occur during capturing and a robust reassembly for matching two broken specimen pieces to extract the specimen's geometry. We compare our results against µCT scans and found that it deviates by 0.057mm on average distributed over the whole specimen for a small amount of 36 captured images. Additionally, comparisons to manually measured values indicate that our system leads to more robust measurements.
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    Fine-Grained Memory Profiling of GPGPU Kernels
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Buelow, Max von; Guthe, Stefan; Fellner, Dieter W.; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    Memory performance is a crucial bottleneck in many GPGPU applications, making optimizations for hardware and software mandatory. While hardware vendors already use highly efficient caching architectures, software engineers usually have to organize their data accordingly in order to efficiently make use of these, requiring deep knowledge of the actual hardware. In this paper we present a novel technique for fine-grained memory profiling that simulates the whole pipeline of memory flow and finally accumulates profiling values in a way that the user retains information about the potential region in the GPU program by showing these values separately for each allocation. Our memory simulator turns out to outperform state-of-theart memory models of NVIDIA architectures by a magnitude of 2.4 for the L1 cache and 1.3 for the L2 cache, in terms of accuracy. Additionally, we find our technique of fine grained memory profiling a useful tool for memory optimizations, which we successfully show in case of ray tracing and machine learning applications.
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    Fitness of General-Purpose Monocular Depth Estimation Architectures for Transparent Structures
    (The Eurographics Association, 2022) Wirth, Tristan; Jamili, Aria; Buelow, Max von; Knauthe, Volker; Guthe, Stefan; Pelechano, Nuria; Vanderhaeghe, David
    Due to material properties, monocular depth estimation of transparent structures is inherently challenging. Recent advances leverage additional knowledge that is not available in all contexts, i.e., known shape or depth information from a sensor. General-purpose machine learning models, that do not utilize such additional knowledge, have not yet been explicitly evaluated regarding their performance on transparent structures. In this work, we show that these models show poor performance on the depth estimation of transparent structures. However, fine-tuning on suitable data sets, such as ClearGrasp, increases their estimation performance on the task at hand. Our evaluations show that high performance on general-purpose benchmarks translates well into performance on transparent objects after fine-tuning. Furthermore, our analysis suggests, that state-of-theart high-performing models are not able to capture a high grade of detail from both the image foreground and background at the same time. This finding shows the demand for a combination of existing models to further enhance depth estimation quality.
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    A GPU Ray Tracing Implementation for Triangular Grid Primitives
    (The Eurographics Association, 2023) Buelow, Max von; Kuijper, Arjan; Fellner, Dieter W.; Abey Campbell; Claudia Krogmeier; Gareth Young
    Triangular grid primitives are a new technique for more efficient handling of memory intensive meshes, also called micro meshes in recent proprietary hardware implementations. This makes it a technique with high potential in the area of virtual environments where hardware capabilities are typically limited. In this poster, we focus on software ray tracing on GPUs and present a novel, easy-to-implement approach that uses a two-level bounding volume hierarchy (BVH) to accelerate these grids. The primary goal of our work is to make the technology more accessible by focusing on standard GPU devices without hardware ray tracing units. With our approach, we are able to encode geometry and BVH with approximately 7.5 bytes per triangle, reducing standard representations by a factor of 3.73 while reducing BVH construction time. Our data structure achieves a peak performance impact of 16% for a three-level subdivision.
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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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    Reconstructing Bounding Volume Hierarchies from Memory Traces of Ray Tracers
    (The Eurographics Association, 2022) Buelow, Max von; Stensbeck, Tobias; Knauthe, Volker; Guthe, Stefan; Fellner, Dieter W.; Yang, Yin; Parakkat, Amal D.; Deng, Bailin; Noh, Seung-Tak
    The ongoing race to improve computer graphics leads to more complex GPU hardware and ray tracing techniques whose internal functionality is sometimes hidden to the user. Bounding volume hierarchies and their construction are an important performance aspect of such ray tracing implementations. We propose a novel approach that utilizes binary instrumentation to collect memory traces and then uses them to extract the bounding volume hierarchy (BVH) by analyzing access patters. Our reconstruction allows combining memory traces captured from multiple ray tracing views independently, increasing the reconstruction result. It reaches accuracies of 30% to 45% when comparing against the ground-truth BVH used for ray tracing a single view on a simple scene with one object. With multiple views it is even possible to reconstruct the whole BVH, while we already achieve 98% with just seven views. Because our approach is largely independent of the data structures used internally, these accurate reconstructions serve as a first step into estimation of unknown construction techniques of ray tracing implementations.
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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.