MAM2024 - MANER Conference London 2024

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Browsing MAM2024 - MANER Conference London 2024 by Subject "Texturing"

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    Neural Texture Block Compression
    (The Eurographics Association, 2024) Fujieda, Shin; Harada, Takahiro; Hardeberg, Jon Yngve; Rushmeier, Holly
    Block compression is a widely used technique to compress textures in real-time graphics applications, offering a reduction in storage size. However, their storage efficiency is constrained by the fixed compression ratio, which substantially increases storage size when hundreds of high-quality textures are required. In this paper, we propose a novel block texture compression method with neural networks, Neural Texture Block Compression (NTBC). NTBC learns the mapping from uncompressed textures to block-compressed textures, which allows for significantly reduced storage costs without any change in the shaders. Our experiments show that NTBC can achieve reasonable-quality results with up to about 45% less storage footprint, preserving real-time performance with a modest computational overhead at the texture loading phase in the graphics pipeline.
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    Psychophysical Insights into Anisotropic Highlights of 3D Printed Objects
    (The Eurographics Association, 2024) Filip, Jirí; Vítek, Martin; Hardeberg, Jon Yngve; Rushmeier, Holly
    3D printing has been extensively used for over two decades by various practitioners and professionals in the industry. This technique, which involves adding material from melted filament layer by layer based on CAD model geometry, imparts a unique appearance to the printed objects. The layering structure generates specific directional reflectance patterns on printed surfaces, leading to anisotropic highlights. Due to slight inaccuracies in the printing setup, the appearance of individual layers is not seamless and exhibits sparkle-like effects along the highlight. In this paper, we conducted a psychophysical experiment to analyze human perception of the printed objects, focusing on the intensity and width of the anisotropic highlights.We discovered that the contrast near the highlights and the variability of pixel intensities along the highlights are highly correlated with human ratings. Lastly, we present a straightforward method utilizing these computational features to enhance the visualization of 3D printed objects.