VMV14

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https://diglib.eg.org/handle/10.2312/7753

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Browsing VMV14 by Subject "Data compaction and compression"

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    Real-time Deblocked GPU Rendering of Compressed Volumes
    (The Eurographics Association, 2014) Marton, Fabio; Guitián, José A. Iglesias; Díaz, Jose; Gobbetti, Enrico; Jan Bender and Arjan Kuijper and Tatiana von Landesberger and Holger Theisel and Philipp Urban
    The wide majority of current state-of-the-art compressed GPU volume renderers are based on block-transform coding, which is susceptible to blocking artifacts, particularly at low bit-rates. In this paper we address the problem for the first time, by introducing a specialized deferred filtering architecture working on block-compressed data and including a novel deblocking algorithm. The architecture efficiently performs high quality shading of massive datasets by closely coordinating visibility- and resolution-aware adaptive data loading with GPU-accelerated per-frame data decompression, deblocking, and rendering. A thorough evaluation including quantitative and qualitative measures demonstrates the performance of our approach on large static and dynamic datasets including a massive 5124 turbulence simulation (256GB), which is aggressively compressed to less than 2 GB, so as to fully upload it on graphics board and to explore it in real-time during animation.
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    Towards Efficient Online Compression of Incrementally Acquired Point Clouds
    (The Eurographics Association, 2014) Golla, Tim; Schwartz, Christopher; Klein, Reinhard; Jan Bender and Arjan Kuijper and Tatiana von Landesberger and Holger Theisel and Philipp Urban
    We present a framework for the online compression of incrementally acquired point cloud data. For this, we extend an existing vector quantization-based offline point cloud compression algorithm to handle the challenges that arise from the envisioned online scenario. In particular, we learn a codebook in advance from training data and replace a computationally demanding part of the algorithm with a faster alternative. We show that the compression ratios and reconstruction quality are comparable to the offline version while the speed is sufficiently improved. Furthermore, we investigate how well codebooks that are generated from different amounts of training data generalize to larger sets of point cloud data.