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A HIDDEN-SURFACE AIC43RITHM with ANTI-ALIASING Edwin Catmull C~Mputer Graphics Lab New York Institute of Technology Old Westbury

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A HIDDEN-SURFACE AIC43RITHM with ANTI-ALIASING Edwin Catmull C~Mputer Graphics Lab New York Institute of Technology Old Westbury A HIDDEN-SURFACE AIC43RITHM WITH ANTI-ALIASING Edwin Catmull C~mputer Graphics Lab New York Institute of Technology Old Westbury, New York 11568 ABSTRACT The techniques for hidden-surface elimination have improved in the last few years with the Suth- In recent years we have gained understanding about erland et al [7] paper providing coherence to the aliasing in computer generated pictures and about development. Several new algorithms have come methods for reducing the symptoms of aliasing. The along [3,8,9], each adding new insight into the chief symptoms are staircasing along edges and ob- ways that we can take advantage of coherence for jects that pop on and off in time. The method for some class of objects to facilitate display. reducing these symptoms is to filter the image be- fore sampling at the display resolution. One Progress for anti-aliasing has been slower. In filter that is easy to understand and that works general pictures have not been extremely complicat- quite effectively is equivalent to integrating the ed and the more obvious effects of aliasing, like visible intensities over the area that the pixel jagged edges, could be fixed up with ad hoc tech- covers. There have been several implementations of niques. Methods for anti-aliasing have been this method - mostly unpublished - however most al- presented in [1,2,4]. Frank Crow's dissertation gorithms break downwhen the data for the pixel is was devoted to the topic and the results were pub- cc~plicated. Unfortunately, as the quality of lished in [2]. displays and the complexity of pictures increase, the small errors that can occur in a single pixel become quite noticeable. A correct solution for ANTI-ALIASING this filter requires a hidden-surface algori~ at each pixel! If the data at the pixel is presented In general, the aliasing problem has been as a depth-ordered list of polygons then the aver- grossly underestimated in computer graphics. Its age visible intensity can be found using a polygon symptoms include: clipper in a way similar to that employed by two known hidden-surface algorithms. All of the po- i. jagged edges lygons in a pixel are clipped against some front 2. small objects popping on and off the screen in unclipped edge into two lists of polygons. The al- successive frames gorithm is recursively entered with each new list 3. moire patterns in rendering periodic images and halts when the front polygon is clipped on all 4. fine detail breaking up. sides, thereby obscuring the polygonsbehind. The area weighted colors are then returned as the value The problem occurs chiefly because image space is to be added to the other pieces in the piyel. sampled at discrete points corresponding to the pixels. Key words: aliasing, clipping, computer graphics, filtering, hidden-surface removal, sampling. There are several unpublished schemes for al- leviating the problem for simple cases - in partic- CR classification: 8.2 ular the symptom of jagged edges. They are unpub- lished because either they are incidental to some INTRODUCTION other algorithm or they are proprietary. Aliasing is now being recognized as an impor- Frank Crow has written about anti-aliasing in tant factor in analysis of image synthesizing algo- [2]. From his study we can extract some key ideas: rithms. Attention has turned to anti-aliasing partly because of the need to refine pictures but i. The image space objects have sharpness and de- mostly because the complexity of scenes has in- tail that cannot possibly be reproduced on a creased and with it the need to have pictures free raster display. It is the attempt to sample of aliasing symptoms. that detail at discrete points in the image that causes the problem. A polygon hidden-surface algorithm is presented here with the focus of attention on anti-aliasing. 2. Point sanpling of an unfiltered object is never One goal has been to produce a "correct" image for correct at any resolution. It is frequently the anti-aliasing technique used. Speed, while im- thought that the symptoms of aliasing will not portant, has played a secondary role. be noticeable if the resolution is high enough. Unfortunately, this is not true. 3. The image should be filtered to eliminate detail The correct integration would be 25% green, 25% that is too fine. After filtering the image black, 50% blue and no red. A simple minded algo- can be sampled. rithm that did not properly take into account what was hidden might distribute the intensities in- One simple filter is to integrate the visible correctly and may even let some red show through. intensities over the area of each pixel. In other Unfortunately for computer graphics our eyes are words we take the average visible intensity over quite capable of seeing errors like these even the square area represented by each pixel if the though they maybe only one millionth of the area image is divided into a rectangular grid. This of the screen. corresponds to convolving the continuous image with a two-dimensional Fourier (box) window. While AN AIC43RITHM WITH ANTI-ALIASING there are better filters, this one is easy to understand and easier to implement analytically In terms of the Sutherland et al criteria the than other filters. The use of this filter will be algorithm presented here: called "area sampling." i. sorts all polygons in y. The difference between point sampling and area sampling is qualitative while the difference 2. sorts all active polygons for a scanline with an between area sampling and better filters is quanti- x-bucketsort. tative. The s~n of all intensities for a point sampled picture will vary as the object is 3. sorts in z by searching a z-list for each enter- translated, ie. for a fine picket fence the picture ing edge. can be all white in one picture and all black in the next. The s~n of all intensities for an area 4. does not use scanline-to-scanline coherence be- sampled picture will be constant under translation cause an x-bucket is used. because area sampling integrates all the intensi- ties. The difference between area sampling and 5. Uses point-to-point coherence since order in the better filters is quantitative since most reason- z-list does not change. able filters would also integrate the intensities. The difference between filtered pictures is lowered While this order of techniques probably has not as the resolution is increased since the sum of in- been used before, it is not new in any spectacular tensities in a local area will be the same or near- way. However, care has been taken to ensure that ly so. We cannot say this when comparing point everything necessary for anti-aliasing is available sampling with sampling of filtered images at high and to a much higher precision than the display. resolution. A line that is much thinner than a pixel will appear dotted using point sampling and The last step is to determine the intensity at jagged using area sampling. As the resolution is the pixel given the z-list. An integrating algo- increased, point sampling will still produce dots rithm is presented here that determines which but area sampling will produce a nice line. pieces of polygons in the pixel are visible and then analytically calculates the average intensity. In order to truly filter the image before sam- pling, an analytic continuous solution to both the Finding which pieces of polygons in the pixel hidden-surface problem and the filter convolution are visible is not unlike the original hidden- must be found. The magnitude of this problem grows surface problem except that we have two simplifica- dranatically with the order of the filter employed. tions: i) we are only interested in the stun of the There are several approaches or simplifications intensities of each piece weighted by its area and that one might take to implement filtering. This 2) the higher level hidden-surface algorithm may paper presents an approach that uses an analytic have already determined the order of the polygons. solution for area sampling. CLIPPING The problem then is to correctly integrate the intensities of all visible objects at a single pix- Clipping is an important part of the algorit/ml. el. This seems to require some kind of hidden- The clipping algorithm used was originally intro- surface algorithm at every pixel! duced in [6]. A variation is presented here for completeness. As an exanple where some algorithms might fail see figure i. When a polygon is clipped against a line it is divided into two polygons. See figure 2. top view After clipping 'l 1 S iblack ,/ green ~ i , j I • . blue black ; cl ipl ine Figure 1 Figure 2 We can determine if a point is on side A or side B line are both specified with polygons. The black by inserting the coordinates of the point into the lines are long thin polygons. equation of the line: d = ax + by + c. If d is less than zero then the point is on side A, otherwise it is on side B. We are going to gen- left t erate an A and a B polygon. i ist The Clipping Algorithm I. A polygon is a list of points PI, P2,...Pn. II. Call Pn the previous point. Determine which Figure 3 side it is on. III. Loop through each point, called the current point. All other polygons in various stages of the al- gorithm are in the more conventional form of a list A. If current point on A side then: of vertices. It is ass~ed that an edge connects the first and last vertex.
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