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Qiuyang Song

dblp:375/7278 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Geometric modeling and processing · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing › shape modeling › parametric modeling › spline curves
b-spline interpolation
0.912025
B-spline curve interpolation to ordered points through shape quality optimization · Comput. Aided Des. 2025
Geometric modeling and processing › curve fitting
curve interpolation
0.912025
B-spline curve interpolation to ordered points through shape quality optimization · Comput. Aided Des. 2025
Geometric modeling and processing › surface fitting
b-spline curve and surface fitting
0.812024
Newton Geometric Iterative Method for B-Spline Curve and Surface Approximation · Comput. Aided Des. 2024
Geometric modeling and processing
geometric iterative method
0.812024
Newton Geometric Iterative Method for B-Spline Curve and Surface Approximation · Comput. Aided Des. 2024

Methods — techniques the papers use, named apart from their topics

shape quality optimization · 0.9parameter correction · 0.8newton iterative method · 0.8
YearPublicationVenuePosition
2025 B-spline curve interpolation to ordered points through shape quality optimization
Qiuyang Song, Pengbo Bo, Jianrui Ding, Caiming Zhang 0001
Comput. Aided Des.2
2024 Newton Geometric Iterative Method for B-Spline Curve and Surface Approximation
abstract
We introduce a progressive and iterative method for B-spline curve and surface approximation, incorporating parameter correction based on the Newton iterative method. While parameter corrections have been used in existing Geometric Approximation (GA) methods to enhance approximation quality, they suffer from low computational efficiency. Our approach unifies control point updates and parameter corrections in a progressive and iterative procedure, employing a one-step strategy for parameter correction. We provide a theoretical proof of convergence for the algorithm, demonstrating its superior computational efficiency compared to current GA methods. Furthermore, the provided convergence proof offers a methodology for proving the convergence of existing GA methods with location parameter correction.
Qiuyang Song, Pengbo Bo
Comput. Aided Des.1