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GPU-Driven Scalable Parser for OBJ Models

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Abstract

This paper presents a scalable parser framework using graphics processing units (GPUs) for massive text-based files. Specifically, our solution is designed to efficiently parse Wavefront OBJ models texts of which specify 3D geometries and their topology. Our work bases its scalability and efficiency on chunk-based processing. The entire parsing problem is subdivided into subproblems the chunk of which can be processed independently and merged seamlessly. The within-chunk processing is made highly parallel, leveraged by GPUs. Our approach thereby overcomes the bottlenecks of the existing OBJ parsers. Experiments performed to assess the performance of our system showed that our solutions significantly outperform the existing CPU-based solutions and GPU-based solutions as well.

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Acknowledgment

Models of Stanford Dragon, XYZ Dragon, XYZ Thai Statue, and Lucy 3D are provided by the courtesy of the Stanford 3D Scanning Repository and the Hairball model by Samuli Laine, Tero Karras, and Morgan McGuire at NVIDIA.

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Authors and Affiliations

  1. Department of Software, Sungkyunkwan University, Suwon, 16419, Korea

    Sunghun Jo, Yuna Jeong & Sungkil Lee

Authors
  1. Sunghun Jo
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  2. Yuna Jeong
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  3. Sungkil Lee
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Corresponding author

Correspondence to Sungkil Lee.

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Jo, S., Jeong, Y. & Lee, S. GPU-Driven Scalable Parser for OBJ Models. J. Comput. Sci. Technol. 33, 417–428 (2018). https://doi.org/10.1007/s11390-018-1827-2

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  • DOI: https://doi.org/10.1007/s11390-018-1827-2

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