苏科华

Supervisor of Doctorate Candidates  
Supervisor of Master's Candidates

E-Mail:

Date of Employment:2008-11-02

School/Department:计算机学院

Education Level:研究生毕业

Business Address:D203

Gender:Male

Contact Information:13517299596

Status:Employed

Discipline:Computer Applications Technology
Communications and Information Systems
Other specialties in Software Engineering
Cyberspace Security


Paper Publications

A measure-driven method for normal mapping and normal map design of 3D models

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Impact Factor:3.6

DOI number:10.1007/s11042-018-6207-y

Affiliation of Author(s):SPRINGER

Journal:MULTIMEDIA TOOLS AND APPLICATIONS

Place of Publication:VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS

Key Words:Normal mapping;Normal map design;Measure-driven;Parameterization

Abstract:Normal mapping is one of the most important methods for photorealistic rendering. It preserves geometric attribute values on a simplified mesh. A normal map stores normal vectors for high-quality meshes in a 2D form. A simplified model is then rendered using these normal vectors. To keep a surface's normal property in a map it first of all requires 2D parameterization. The most common approach to this is to divide the surface into several patches, where each patch has its own parameterization. However, this approach has some weakness when it comes to designing global normal maps. This paper presents a measure-driven method that can interactively direct design of normal maps on a 2D plane. This 2D plane has minimal distortion and, more importantly, it is possible to zoom in or shrink the area of interest. The resulting, novel framework serves as a powerful tool for normal mapping and normal map design. We provide a variety of experimental results to demonstrate the efficiency, robustness and efficacy of our approach.

Co-author:Li Yinghua,Zhang Jialing

First Author:Qian Kun

Indexed by:Article

Correspondence Author:Su Kehua

Document Type:J

Volume:77

Issue:24

Page Number:31969-31989

ISSN No.:1380-7501

Translation or Not:no

Date of Publication:2018-12-06

Included Journals:SCI、EI

Profile

苏科华,男,武汉大学计算机学院教授、博导;武汉大学科技成果转化中心(技术转移中心)副主任。研究主要集中在最优传输(Optimal Transport)领域,它是研究概率测度间最优变换的一类优化问题。在计算机图形学、机器视觉、人工智能、医学图像处理等领域有着广泛的应用。本人主要研究最优传输的几何计算理论和高效算法,并将其应用于网格保测参数化、三维场景优化、智能烧伤评估和卫星互联网任务优化中。主持包括国家自然科学基金、中央军科委、航天5院、华为公司等20多个项目支持,发表论文50余篇,获批发明专利10余项。为CCF计算机辅助设计与图形学(CAD/CG)和虚拟现实与可视化(TCVRV)专委会执行委员。