Browsing by Author "Wirth, Tristan"

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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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    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 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, Raphaëlle; 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.