ISCAS OpenIR
kd-tree based parallel adaptive rendering
Liu Xiao-Dan; Wu Jia-Ze; Zheng Chang-Wen
2012
发表期刊Visual Computer
ISSN1782789
卷号28期号:41068页码:613-623
摘要Multidimensional adaptive sampling technique is crucial for generating high quality images with effects such as motion blur, depth-of-field and soft shadows, but it costs a lot of memory and computation time. We propose a novel kd-tree based parallel adaptive rendering approach. First, a two-level framework for adaptive sampling in parallel is introduced to reduce the computation time and control the memory cost: in the prepare stage, we coarsely sample the entire multidimensional space and use kd-tree structure to separate it into several multidimensional subspaces; in the main stage, each subspace is refined by a sub kd-tree and rendered in parallel. Second, novel kd-tree based strategies are introduced to measure space's error value and generate anisotropic Poisson disk samples. The experimental results show that our algorithm produces better quality images than previous ones. © 2012 Springer-Verlag.
收录类别ei
部门归属(1) National Key Laboratory of Integrated Information System Technology, Institute of Software, Chinese Academy of Sciences, Beijing, China; (2) Graduate University of Chinese Academy of Sciences, Beijing, China
语种英语
WOS记录号WOS:000304411500010
引用统计
内容类型期刊论文
URI标识http://ir.iscas.ac.cn/handle/311060/14708
专题中国科学院软件研究所
推荐引用方式
GB/T 7714
Liu Xiao-Dan,Wu Jia-Ze,Zheng Chang-Wen. kd-tree based parallel adaptive rendering[J]. Visual Computer,2012,28(41068):613-623.
APA Liu Xiao-Dan,Wu Jia-Ze,&Zheng Chang-Wen.(2012).kd-tree based parallel adaptive rendering.Visual Computer,28(41068),613-623.
MLA Liu Xiao-Dan,et al."kd-tree based parallel adaptive rendering".Visual Computer 28.41068(2012):613-623.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
e55689560135228g.pdf(2700KB) 开放获取使用许可请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Liu Xiao-Dan]的文章
[Wu Jia-Ze]的文章
[Zheng Chang-Wen]的文章
百度学术
百度学术中相似的文章
[Liu Xiao-Dan]的文章
[Wu Jia-Ze]的文章
[Zheng Chang-Wen]的文章
必应学术
必应学术中相似的文章
[Liu Xiao-Dan]的文章
[Wu Jia-Ze]的文章
[Zheng Chang-Wen]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。