Adaptive sampling and reconstruction for gradient-domain rendering

作     者：Yuzhi Liang Tao Liu Yuchi Huo Rui Wang Hujun Bao 

作者机构：State Key Lab of CAD&CGZhejiang UniversityHangzhou 310058China College of Transport and CommunicationsShanghai Maritime UniversityShanghai 201306China 

出 版 物：《Computational Visual Media》 (计算可视媒体（英文版）)

年 卷 期：2024年第10卷第5期

页      面：885-902页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：supported by the Key R&D Program of Zhejiang Province(No.2023C01039) 

主　　题：gradient-domain rendering adaptive rendering Monte Carlo rendering deep learning-based Monte Carlo denoising 

摘      要：Gradient-domain rendering estimates finite difference gradients of image intensities and reconstructs the final result by solving a screened Poisson problem,which shows improvements over merely sampling pixel *** sampling is another orthogonal research area that focuses on distributing samples adaptively in the primal ***,adaptive sampling in the gradient domain with low sampling budget has been less *** idea is based on the observation that signals in the gradient domain are sparse,which provides more flexibility for adaptive *** propose a deep-learning-based end-to-end sampling and reconstruction framework in gradient-domain rendering,enabling adaptive sampling gradient and the primal maps *** conducted extensive experiments for evaluation and showed that our method produces better reconstruction quality than other methods in the test dataset.