Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (9): 188-195.doi: 10.11896/jsjkx.260600137

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Efficient GPU-parallel Algorithm for B-spline Curve Offsetting

YANG Yixuan1,3, WANG Weiming2,3, YANG Zhouwang1,3   

  1. 1 School of Mathematical Sciences,University of Science and Technology of China,Hefei 230026,China
    2 School of Mathematics and Statistics,Nanjing University of Science and Technology,Nanjing 210094,China
    3 Hefei Institute of Artificial Intelligence and Big Data Research,Hefei 230000,China
  • Received:2026-03-03 Revised:2026-06-21 Online:2026-09-15 Published:2026-09-10
  • About author:YANG Yixuan,born in 2005,bachelor.His main research interest is optimization algorithms.
    WANG Weiming,born in 1986,Ph.D,professor.His main research interests include geometry processing and computational fabrication.
  • Supported by:
    National Natural Science Foundation of China Major Research Program (92270205),Major Science and Techno-logy Research Program of Anhui Province(202423e09050003),Natural Science Foundation of Chongqing,China(CSTB2025NSCQ-LZX0056)and Fundamental Research Funds for the Central Universities(2026101001).

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Abstract: To address the demand for efficient processing of large-scale B-spline curve offsetting and self-intersection removal in fields such as integrated circuit layout design,CAD/CAM,and EDA,traditional B-spline offset algorithms,although capable of controlling the number of control points after offsetting to a certain extent,are difficult to implement efficiently on GPUs in a unified computational workflow.To tackle this problem,and considering the differences among existing B-spline offset algorithms in terms of the number of control points after offsetting as well as the efficiency of related algorithms in OpenCascade,this paper proposes a GPU-CPU collaborative accelerated algorithm for B-spline curve offsetting.Within this framework,both the offset computation and intersection detection are executed on the GPU.Firstly,the offset curve is partitioned into segments and appro-ximated using Legendre polynomials,forming a B-spline offset algorithm with a subdivision stage that avoids branch divergence,and ultimately producing piecewise Bézier curves.Subsequently,spatial grid partitioning is performed on the GPU for each Bézier segment.The grid resolution is adaptively determined according to the average size of the bounding boxes,and a spatial index is constructed using the compressed sparse row(CSR) format.During the candidate pair filtering stage,duplicate candidate pairs are removed using a canonical cell deduplication strategy,followed by narrow-phase filtering based on convex hull intersection tests.Finally,intersections are computed in parallel for the filtered candidate curve segments,and the resulting intersection information is returned to the CPU for topological reconstruction,thereby effectively avoiding self-intersections generated after offsetting.The proposed algorithm is evaluated on geometrically complex curves as well as real EDA layout datasets,and the experimental results demonstrate the effectiveness and computational efficiency of the proposed method.

Key words: B-spline offset, Self-intersection, GPU-optimized, Parallel computing

CLC Number: 

  • TP391
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