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"authors": [
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"abstract": "Abstract—A terrain is most often represented with a digital elevation map consisting of a set of sample points from the terrain surface. This paper presents a fast and practical algorithm to compute the horizon, or skyline, at all sample points of a terrain. The horizons are useful in a number of applications, including the rendering of self-shadowing displacement maps, visibility culling for faster flight simulation, and rendering of cartographic data. Experimental and theoretical results are presented which show that the algorithm is more accurate that previous algorithms and is faster than previous algorithms in terrains of more than 100,000 sample points.",
"abstracts": [
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"content": "Abstract—A terrain is most often represented with a digital elevation map consisting of a set of sample points from the terrain surface. This paper presents a fast and practical algorithm to compute the horizon, or skyline, at all sample points of a terrain. The horizons are useful in a number of applications, including the rendering of self-shadowing displacement maps, visibility culling for faster flight simulation, and rendering of cartographic data. Experimental and theoretical results are presented which show that the algorithm is more accurate that previous algorithms and is faster than previous algorithms in terrains of more than 100,000 sample points.",
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"abstract": "Poisson disk sampling has excellent spatial and spectral properties, and plays an important role in a variety of visual computing. Although many promising algorithms have been proposed for multidimensional sampling in euclidean space, very few studies have been reported with regard to the problem of generating Poisson disks on surfaces due to the complicated nature of the surface. This paper presents an intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces. In sharp contrast to the conventional parallel approaches, our method neither partitions the given surface into small patches nor uses any spatial data structure to maintain the voids in the sampling domain. Instead, our approach assigns each sample candidate a random and unique priority that is unbiased with regard to the distribution. Hence, multiple threads can process the candidates simultaneously and resolve conflicts by checking the given priority values. Our algorithm guarantees that the generated Poisson disks are uniformly and randomly distributed without bias. It is worth noting that our method is intrinsic and independent of the embedding space. This intrinsic feature allows us to generate Poisson disk patterns on arbitrary surfaces in IRn. To our knowledge, this is the first intrinsic, parallel, and accurate algorithm for surface Poisson disk sampling. Furthermore, by manipulating the spatially varying density function, we can obtain adaptive sampling easily.",
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"content": "Poisson disk sampling has excellent spatial and spectral properties, and plays an important role in a variety of visual computing. Although many promising algorithms have been proposed for multidimensional sampling in euclidean space, very few studies have been reported with regard to the problem of generating Poisson disks on surfaces due to the complicated nature of the surface. This paper presents an intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces. In sharp contrast to the conventional parallel approaches, our method neither partitions the given surface into small patches nor uses any spatial data structure to maintain the voids in the sampling domain. Instead, our approach assigns each sample candidate a random and unique priority that is unbiased with regard to the distribution. Hence, multiple threads can process the candidates simultaneously and resolve conflicts by checking the given priority values. Our algorithm guarantees that the generated Poisson disks are uniformly and randomly distributed without bias. It is worth noting that our method is intrinsic and independent of the embedding space. This intrinsic feature allows us to generate Poisson disk patterns on arbitrary surfaces in IRn. To our knowledge, this is the first intrinsic, parallel, and accurate algorithm for surface Poisson disk sampling. Furthermore, by manipulating the spatially varying density function, we can obtain adaptive sampling easily.",
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"title": "Mixed Tensor Product of q-Bezier-Poisson Surfaces",
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"title": "Improving Transparent Visualization of Large-Scale Laser-Scanned Point Clouds by Using Poisson Disk Sampling",
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End of preview. Expand
in Dataset Viewer.
Dataset Card for "TVCG_Papers"
Dataset Description Repository: Paper: Leaderboard: Point of Contact:
Dataset Summary: This dataset contains 5178 papers from IEEE TVCG. It contains multiple raw attributes of each paper, including both meta-data and abstract.
Dataset Structure: Jsonl file, each paper instance is a json object.
Data Fields:
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Source Data: https://www.computer.org/csdl/journal/tg
Citation Information: Yamei Tu (tu.253@osu.edu)
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