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Journal articles on the topic 'Rasterization'

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Published: 4 June 2021
Last updated: 19 February 2022

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1

Manson, J., and S. Schaefer. "Wavelet Rasterization." Computer Graphics Forum 30, no. 2 (April 2011): 395–404. http://dx.doi.org/10.1111/j.1467-8659.2011.01887.x.

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2

Popescu, Voicu, and Paul Rosen. "Forward rasterization." ACM Transactions on Graphics 25, no. 2 (April 2006): 375–411. http://dx.doi.org/10.1145/1138450.1138460.

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3

Meissner, M., D. Bartz, R. Gunther, and W. Strasser. "Visibility Driven Rasterization." Computer Graphics Forum 20, no. 4 (December 2001): 283–94. http://dx.doi.org/10.1111/1467-8659.00555.

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4

Hobby, John D. "Rasterization of nonparametric curves." ACM Transactions on Graphics 9, no. 3 (July 1990): 262–77. http://dx.doi.org/10.1145/78964.78966.

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5

Shinya, Mikio, and Marie-Claire Forgue. "Interference detection through rasterization." Journal of Visualization and Computer Animation 2, no. 4 (October 1991): 132–34. http://dx.doi.org/10.1002/vis.4340020408.

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6

Balkin, Sandy D., Elizabeth L. Golebiewski, and Clifford A. Reiter. "Rasterization of elliptic curves." Computers & Graphics 18, no. 6 (November 1994): 837–43. http://dx.doi.org/10.1016/0097-8493(94)90010-8.

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7

Yu, Xuan, Jingyi Yu, and Leonard McMillan. "Towards multi-perspective rasterization." Visual Computer 25, no. 5-7 (March 19, 2009): 549–57. http://dx.doi.org/10.1007/s00371-009-0335-3.

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8

CHEN, CHENG-HSIEN, and CHEN-YI LEE. "REDUCE THE MEMORY BANDWIDTH OF 3D GRAPHICS HARDWARE WITH A NOVEL RASTERIZER." Journal of Circuits, Systems and Computers 11, no. 04 (August 2002): 377–91. http://dx.doi.org/10.1142/s0218126602000525.

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Currently, memory bandwidth has become the main bottleneck in graphics system. Reducing the memory access can reduce the power consumption and boost overall system performance. Low power technique is more important for graphics applications on hand-held or mobile device. In this paper, we propose a novel visibility driven rasterizer to reduce the memory access and operations on invisible pixels. It integrates with two-level hierarchical Z-buffer to do visibility driven rasterization. The rasterization scheme is tile-order scan-line based, and the rasterizer can smartly change the tile-size depending on the triangle size. This technique can balance the rasterization loading under different triangles. Moreover, we propose a fast visibility test algorithm to quickly reject a group of pixels within the tile. Simulation results show that the overall bandwidth reduction can be up to 60% under our test images.
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9

Hu, Wei, Yangyu Huang, Fan Zhang, Guodong Yuan, and Wei Li. "Ray tracing via GPU rasterization." Visual Computer 30, no. 6-8 (May 8, 2014): 697–706. http://dx.doi.org/10.1007/s00371-014-0968-8.

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10

Dychka, Ivan A., Yevgeniya S. Sulema, and Denys Chernykh. "Rasterization Method for Voxel Model Cutting." Research Bulletin of the National Technical University of Ukraine "Kyiv Politechnic Institute", no. 2 (June 12, 2018): 25–32. http://dx.doi.org/10.20535/1810-0546.2018.2.129009.

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11

Pineda, Juan. "A parallel algorithm for polygon rasterization." ACM SIGGRAPH Computer Graphics 22, no. 4 (August 1988): 17–20. http://dx.doi.org/10.1145/378456.378457.

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12

Gharachorloo, N., S. Gupta, R. F. Sproull, and I. E. Sutherland. "A characterization of ten rasterization techniques." ACM SIGGRAPH Computer Graphics 23, no. 3 (July 1989): 355–68. http://dx.doi.org/10.1145/74334.74370.

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13

Zhou, Chen, Dingmou Li, Ningchuan Xiao, Zhenjie Chen, Xiang Li, and Manchun Li. "A topology-preserving polygon rasterization algorithm." Cartography and Geographic Information Science 45, no. 6 (November 21, 2017): 495–509. http://dx.doi.org/10.1080/15230406.2017.1401488.

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14

Klassen, R. Victor. "A SIMD parallel trapezoid rasterization algorithm." Computers & Graphics 15, no. 4 (January 1991): 553–59. http://dx.doi.org/10.1016/0097-8493(91)90056-n.

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15

Tao, You Long. "Triangle Rasterizer Based on the Barycentric Arithmetic." Applied Mechanics and Materials 519-520 (February 2014): 697–702. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.697.

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A triangle rasterizer features the barycentric arithmetic for rasterization operation in graphics processors is introduced in this paper. First, the common method for triangle rasterization is presented, and its drawbacks of lacking simplicity and introducing accumutive errors are highlighted. Then, the new approach using the barycentric arithmetic is discussed in detail, including finding the pixels belong to the triangle and interpolating attributes for these pixels. At the basis of that, the hardware structure of the rasterizer is proposed with elaboration of the bounding rectangle establishment and the pipelined divider in it. At last, the RTL simulation result of the proposed rasterizer is given and analyzed, which has shown that the rasterizer can generate pixels efficiently and accurately.
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16

Stefanakis, Emmanuel. "mR-V: Line Simplification through Mnemonic Rasterization." GEOMATICA 70, no. 4 (December 2016): 269–82. http://dx.doi.org/10.5623/cig2016-401.

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Linear features (e.g., roads, rivers), outlines of areas (e.g., municipal boundaries, lake banks) or mov ing objects’ trajectories (e.g., humans, vehicles) are represented on paper or digital maps with polyline geometries. Sampling, discretization and generalization processes result in polylines represented by a subset of vertices of the original line. The simplified version of a linear feature may violate some spatial relations (topological, direction or distance) that apply between the original line and some other objects in space, unless a model that considers the context of the neighbouring space is applied. The latter turns the line sim plification into a rather complicated and challenging-to-automate process. This paper introduces a method that supports a consistent line simplification without considering the context of the neighbouring space. The method applies well-known geo-processing tasks, such as polyline-to-raster and raster-topoly l ine conversions, and is compliant with the raster tiled maps as well as the discrete global grid systems.
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17

Lee, Jin-Aeon, and Lee-Sup Kim. "SPARP: a single pass antialiased rasterization processor." Computers & Graphics 24, no. 2 (April 2000): 233–43. http://dx.doi.org/10.1016/s0097-8493(99)00157-0.

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18

Munkberg, Jacob, Robert Toth, and Tomas Akenine-Möller. "Per-Vertex Defocus Blur for Stochastic Rasterization." Computer Graphics Forum 31, no. 4 (June 2012): 1385–89. http://dx.doi.org/10.1111/j.1467-8659.2012.03133.x.

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19

Manson, Josiah, and Scott Schaefer. "Analytic Rasterization of Curves with Polynomial Filters." Computer Graphics Forum 32, no. 2pt4 (May 2013): 499–507. http://dx.doi.org/10.1111/cgf.12070.

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20

Schmitz, A., T. Rick, T. Karolski, T. Kuhlen, and L. Kobbelt. "Efficient Rasterization for Outdoor Radio Wave Propagation." IEEE Transactions on Visualization and Computer Graphics 17, no. 2 (February 2011): 159–70. http://dx.doi.org/10.1109/tvcg.2010.96.

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21

Li, Rui, Qiming Hou, and Kun Zhou. "Efficient GPU path rendering using scanline rasterization." ACM Transactions on Graphics 35, no. 6 (November 11, 2016): 1–12. http://dx.doi.org/10.1145/2980179.2982434.

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22

Moser, T. J. "Point-to-curve raytracing by algebraic rasterization." Studia Geophysica et Geodaetica 50, no. 3 (July 2006): 399–416. http://dx.doi.org/10.1007/s11200-006-0025-9.

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23

Fang, De Jian. "Deconstructing Forward-Error Correction." Applied Mechanics and Materials 151 (January 2012): 602–6. http://dx.doi.org/10.4028/www.scientific.net/amm.151.602.

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Hash tables must work. In fact, few experts would disagree with the development of rasterization, which embodies the natural principles of cryptoanalysis. We present new distributed symmetries, which we call NOG.
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24

Moeckel, Rolf, and Rick Donnelly. "Gradual rasterization: redefining spatial resolution in transport modelling." Environment and Planning B: Planning and Design 42, no. 5 (January 2015): 888–903. http://dx.doi.org/10.1068/b130199p.

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25

Laine, Samuli, Timo Aila, Tero Karras, and Jaakko Lehtinen. "Clipless dual-space bounds for faster stochastic rasterization." ACM Transactions on Graphics 30, no. 4 (July 1, 2011): 1. http://dx.doi.org/10.1145/2010324.1965001.

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26

Frolov, Vladimir Alexandrovich, Vladimir Alexandrovich Galaktionov, and Boris Haimovich Barladyan. "Comparative study of high performance software rasterization techniques." Mathematica Montisnigri 47 (2020): 152–75. http://dx.doi.org/10.20948/mathmontis-2020-47-13.

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27

Friston, Sebastian, Tobias Ritschel, and Anthony Steed. "Perceptual rasterization for head-mounted display image synthesis." ACM Transactions on Graphics 38, no. 4 (July 12, 2019): 1–14. http://dx.doi.org/10.1145/3306346.3323033.

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28

Li, Tzu-Mao, Michal Lukáč, Michaël Gharbi, and Jonathan Ragan-Kelley. "Differentiable vector graphics rasterization for editing and learning." ACM Transactions on Graphics 39, no. 6 (November 26, 2020): 1–15. http://dx.doi.org/10.1145/3414685.3417871.

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29

Kim, Donghyun, and Lee-Sup Kim. "Area-efficient pixel rasterization and texture coordinate interpolation." Computers & Graphics 32, no. 6 (December 2008): 669–81. http://dx.doi.org/10.1016/j.cag.2008.08.007.

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30

Zakharov, Ivan S., V. M. Dovgal, and V. L. Markovchin. "Method and Algorithm for Rasterization of Point Graphic Objects." Telecommunications and Radio Engineering 69, no. 3 (2010): 191–206. http://dx.doi.org/10.1615/telecomradeng.v69.i3.10.

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31

Park, Jinhong, Jinwoo Kim, Woo-Chan Park, Youngsik Kim, Chelho Jeong, and Tack-Don Han. "An effective rasterization architecture for mobile vector graphics processors." IEICE Electronics Express 8, no. 11 (2011): 835–41. http://dx.doi.org/10.1587/elex.8.835.

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32

Yamashita, Yoshiyuki. "Implementing a Rasterization Framework for a Black Hole Spacetime." Journal of Information Processing 24, no. 4 (2016): 690–99. http://dx.doi.org/10.2197/ipsjjip.24.690.

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33

Niu, Lianqiang, and Zhong Shao. "A Fast Line Rasterization Algorithm Based on Pattern Decomposition." Journal of Computer-Aided Design & Computer Graphics 22, no. 8 (September 6, 2010): 1286–92. http://dx.doi.org/10.3724/sp.j.1089.2010.10949.

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34

Wüthrich, Charles A. "A model for curve rasterization in n-dimensional space." Computers & Graphics 22, no. 2-3 (March 1998): 153–60. http://dx.doi.org/10.1016/s0097-8493(98)00001-6.

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35

Liu, B., G. J. Clapworthy, and F. Dong. "Fast Isosurface Rendering on a GPU by Cell Rasterization." Computer Graphics Forum 28, no. 8 (December 2009): 2151–64. http://dx.doi.org/10.1111/j.1467-8659.2009.01422.x.

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36

O'neal, Kenneth, Philip Brisk, Ahmed Abousamra, Zack Waters, and Emily Shriver. "GPU Performance Estimation using Software Rasterization and Machine Learning." ACM Transactions on Embedded Computing Systems 16, no. 5s (October 10, 2017): 1–21. http://dx.doi.org/10.1145/3126557.

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37

Ackermann, Hans-Josef. "Single chip hardware support for rasterization and texture mapping." Computers & Graphics 20, no. 4 (July 1996): 503–14. http://dx.doi.org/10.1016/0097-8493(96)00022-2.

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38

Brimkov, Valentin E., Reneta P. Barneva, and Boris Brimkov. "Connected distance-based rasterization of objects in arbitrary dimension." Graphical Models 73, no. 6 (November 2011): 323–34. http://dx.doi.org/10.1016/j.gmod.2011.06.002.

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39

Yang, Ming, and Hongyang Chao. "ECISER: Efficient Clip-art Image SEgmentation by Re-rasterization." Computer-Aided Design 58 (January 2015): 105–16. http://dx.doi.org/10.1016/j.cad.2014.08.011.

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40

van Lierop, M. L. P., C. W. A. M. van Overveld, and H. M. M. van de Wetering. "Line rasterization algorithms that satisfy the subset line property." Computer Vision, Graphics, and Image Processing 41, no. 2 (February 1988): 210–28. http://dx.doi.org/10.1016/0734-189x(88)90020-5.

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41

Walewski, Patryk, Tomasz Gałaj, and Dominik Szajerman. "Heuristic based real-time hybrid rendering with the use of rasterization and ray tracing method." Open Physics 17, no. 1 (October 4, 2019): 527–44. http://dx.doi.org/10.1515/phys-2019-0055.

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Abstract Nowadays, rasterization is the most common method used to achieve real-time semi-photorealistic effects in games or interactive applications. Some of those effects are not easily achievable, thus require more complicated methods and are difficult to obtain. The appearance of the presented worlds depends to a large extent on the approximation to the physical basis of light behaviour in them. The best effects in this regard are global illumination algorithms. Each of them including ray tracing give the most plausible effects, but at cost of higher computational complexity. Today’s hardware allows usage of ray tracing methods in-real time on Graphics Processing Units (GPU) thanks to its parallel nature. However, using ray tracing as a single rendering method may still result in poor performance, especially when used to create many image effects in complex environments. In this paper we present a hybrid approach for real-time rendering using both rasterization and ray tracing using heuristic, which determines whether to render secondary effects such as shadows, reflections and refractions for individual objects considering their relevancy and cost of rendering those effects for these objects in particular case.
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42

GAO, Yi, Jianxin LUO, Hangping QIU, Bin TANG, Bo WU, and Weiwei DUAN. "A GPU-Based Rasterization Algorithm for Boolean Operations on Polygons." IEICE Transactions on Information and Systems E101.D, no. 1 (2018): 234–38. http://dx.doi.org/10.1587/transinf.2017edl8119.

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43

KIKUGAWA, Makoto, and Kouji KOYAMADA. "An efficient voxelization algorithm with a two-pass rasterization technique." Journal of the Visualization Society of Japan 24, Supplement1 (2004): 447–48. http://dx.doi.org/10.3154/jvs.24.supplement1_447.

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44

Gorte, Ben, and Sisi Zlatanova. "RASTERIZATION AND VOXELIZATION OF TWO- AND THREE-DIMENSIONAL SPACE PARTITIONINGS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 13, 2016): 283–88. http://dx.doi.org/10.5194/isprsarchives-xli-b4-283-2016.

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The paper presents a very straightforward and effective algorithm to convert a space partitioning, made up of polyhedral objects, into a 3D block of voxels, which is fully occupied, i.e. in which every voxel has a value. In addition to walls, floors, etc. there are 'air' voxels, which in turn may be distinguished as indoor and outdoor air. The method is a 3D extension of a 2D polygon-to-raster conversion algorithm. The input of the algorithm is a set of non-overlapping, closed polyhedra, which can be nested or touching. The air volume is not necessarily represented explicitly as a polyhedron (it can be treated as 'background', leading to the 'default' voxel value). The approach consists of two stages, the first being object (boundary) based, the second scan-line based. In addition to planar faces, other primitives, such as ellipsoids, can be accommodated in the first stage without affecting the second.
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45

Yang Shouliang, Yang Baoliang, and Bao Songjian. "A Methodology for the Simulation of Rasterization in Cognitive Network." International Journal of Advancements in Computing Technology 5, no. 4 (February 28, 2013): 243–49. http://dx.doi.org/10.4156/ijact.vol5.issue4.30.

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46

Gorte, Ben, and Sisi Zlatanova. "RASTERIZATION AND VOXELIZATION OF TWO- AND THREE-DIMENSIONAL SPACE PARTITIONINGS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 13, 2016): 283–88. http://dx.doi.org/10.5194/isprs-archives-xli-b4-283-2016.

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The paper presents a very straightforward and effective algorithm to convert a space partitioning, made up of polyhedral objects, into a 3D block of voxels, which is fully occupied, i.e. in which every voxel has a value. In addition to walls, floors, etc. there are 'air' voxels, which in turn may be distinguished as indoor and outdoor air. The method is a 3D extension of a 2D polygon-to-raster conversion algorithm. The input of the algorithm is a set of non-overlapping, closed polyhedra, which can be nested or touching. The air volume is not necessarily represented explicitly as a polyhedron (it can be treated as 'background', leading to the 'default' voxel value). The approach consists of two stages, the first being object (boundary) based, the second scan-line based. In addition to planar faces, other primitives, such as ellipsoids, can be accommodated in the first stage without affecting the second.
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47

McManus, Donald, and Carl Beckmann. "Optimal static 2-dimensional screen subdivision for parallel rasterization architectures." Computers & Graphics 21, no. 2 (March 1997): 159–69. http://dx.doi.org/10.1016/s0097-8493(96)00079-9.

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48

Tong, Ting-Chi, and Yun-Nan Chang. "Efficient Vector Graphics Rasterization Accelerator Using Optimized Scan-Line Buffer." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 21, no. 7 (July 2013): 1246–59. http://dx.doi.org/10.1109/tvlsi.2012.2207413.

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49

Woo-Chan Park, Kil-Whan Lee, Il-San Kim, Tack-Don Han, and Sung-Bong Yang. "An effective pixel rasterization pipeline architecture for 3d rendering processors." IEEE Transactions on Computers 52, no. 11 (November 2003): 1501–8. http://dx.doi.org/10.1109/tc.2003.1244948.

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50

Guan, Yanxin, Xinzhu Sang, Shujun Xing, Yingying Chen, Yuanhang Li, Duo Chen, Xunbo Yu, and Binbin Yan. "Parallel multi-view polygon rasterization for 3D light field display." Optics Express 28, no. 23 (October 28, 2020): 34406. http://dx.doi.org/10.1364/oe.408857.

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