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图学学报

图学学报 ›› 2021, Vol. 42 ›› Issue (1): 101-109.DOI: 10.11996/JG.j.2095-302X.2021010101

• 计算机图形学与虚拟现实 • 上一篇    下一篇

基于稀疏超采样的时间性反走样算法 

  

  1. 四川大学视觉合成图形图像技术国防重点学科实验室，四川 成都 610065
  • 出版日期:2021-02-28 发布日期:2021-02-01
  • 基金资助:
    国家自然科学基金面上项目(61472261)；国家高技术研究发展计划(863计划)(2015AA016405)

Temporal anti-aliasing algorithm based on sparse super-sampling 

  1. National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu Sichuan 610065, China
  • Online:2021-02-28 Published:2021-02-01
  • Supported by:
    National Natural Science Foundation of China (61472261); The National High Technology Research and Development Program of China (2015AA016405) 

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摘要： 针对时间性反走样算法在处理帧间复用时，若场景中有较多高频颜色区域或精细模型会造成重 影、模糊、闪烁及子像素细节丢失的问题，提出了基于稀疏超采样的时间性反走样算法。基本思想是，在时间 性反走样算法的基础上，对于无法复用历史帧像素，重新引入空间域的超采样，利用剔除算法以避免不必要的 绘制开销，实现对场景的稀疏超采样。实验结果表明，该算法能够得到与超采样算法媲美的反走样效果，并具 有更高的渲染效率，有效避免重影、模糊、闪烁及子像素细节丢失的问题。 

关键词: 时间性反走样算法, 稀疏, 超采样, 剔除, 复用

Abstract: In order to deal with the problems of the temporal anti-aliasing algorithm, such as ghosting, blurring, flickering, and loss of sub-pixel details when processing multiplexing between frames, in the cases of many high-frequency color regions or fine models in the scene, this paper proposed the temporal anti-aliasing algorithm based on sparse super-sampling. The core idea was that, based on the temporal anti-aliasing algorithm, for pixels that cannot reuse historical frames, super-sampling in the spatial domain was re-introduced, and the culling algorithm proposed in this paper was employed to avoid unnecessary drawing overhead and achieve sparse super-sampling. Experimental results show that the algorithm in this paper can obtain the anti-aliasing effect comparable to the super-sampling algorithm, and achieve higher rendering efficiency, which can effectively avoid the problems of ghosting, blurring, flickering, and loss of sub-pixel details. 

Key words:  , temporal anti-aliasing algorithm, sparse, super-sampling, culling, reuse 

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