In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorith...In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.展开更多
基金This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61372168, 61373071, 61372190, and 61331018, the Scientific Research Foundation for the Returned Overseas Chinese Scholars of State Education Ministry of China, the Visual Computing Center at King Abudullah University of Science and Technology （KAUST）, and the Open Funding Project of the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, under Grant Nos. BUAA-VR- 15KF-06 and BUAA-VR-14KF-10.
文摘In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.
