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# UCSD CSE 167 - Viewing & Perspective

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Viewing & PerspectiveCSE167: Computer GraphicsInstructor: Steve RotenbergUCSD, Fall 2005Homogeneous Transformations1 0 00 1110001+++=+++=′+++=′+++=′⋅=′′′⋅=′zyxzzzyzxzzyzyyyxyyxzxyxxxxzyxzzzzyyyyxxxxzyxvvvdvcvbvavdvcvbvavdvcvbvavvvvdcbadcbadcbavvvvMv3D TransformationsSo far, we have studied a variety of useful 3D transformations:RotationsTranslationsScalesShearsReflectionsThese are examples of affine transformationsThese can all be combined into a single 4x4 matrix, with [0 0 0 1] on the bottom rowThis implies 12 constants, or 12 degrees of freedom (DOFs)ABCD VectorsWe mentioned that the translation information is easily extracted directly from the matrix, while the rotation/scale/shear/reflection information is encoded into the upper 3x3 portion of the matrixThe 9 constants in the upper 3x3 matrix make up 3 vectors called a, b, and cIf we think of the matrix as a transformation from object space to world space, then the a vector is essentially the object’s x-axis transformed into world space, b is its y-axis in world space, and c is its z-axis in world spaced is of course the position in world space.Example: YawA spaceship is floating out in space, with a matrix W. The pilot wants to turn the ship 10 degrees to the left (yaw). Show how to modify W to achieve this.Example: YawWe simply rotate W around its own b vector, using the ‘arbitrary axis rotation’ matrix:where Ra(a,θ)=−++−−−−−−++−+−−−−+10000)1()1()1(0)1()1()1(0)1()1()1(222222zzxzyyzxxzyyyzyxyzxzyxxxacasacaasacaasacaaacasacaasacaasacaaacaθθθθθθθθθθθθθθθ( ) ( ) ( )WMWdWTbWRdWTM⋅=′−⋅°⋅= .10,..aSpacesSo far, we’ve discussed the following spacesObject space (local space)World space (global space)Camera spaceCamera SpaceLet’s say we want to render an image of a chair from a certain camera’s point of viewThe chair is placed in world space with matrix WThe camera is placed in world space with matrix CThe following transformation takes vertices from the chair’s object space into world space, and then from world space into camera space:Now that we have the object transformed into a space relative to the camera, we can focus on the next step, which is to project this 3D space into a 2D image spacevWCv ⋅⋅=′−1Image SpaceWhat we really need is a mapping from 3D space into a special “2.5D” image spaceFor practical geometric purposes, it should be thought of as a proper 2D space, but each vertex in this space also has a depth (z coordinate), and so mathematically speaking, it really is a 3D space…We will say that the visible portion of the image ranges from -1 to 1 in both x and y, with 0,0 in the center of the imageThe z coordinate will also range from -1 to 1 and will represent the depth (1 being nearest and -1 being farthest)Image space is sometimes called normalized view space or other things…View ProjectionsSo far we know how to transform objects from object space into world space and into camera spaceWhat we need now is some sort of transformation that takes points in 3D camera space and transforms them into our 2.5D image spaceWe will refer to this class of transformations as view projections (or just projections)Simple orthographic view projections can just be treated as one more affine transformation applied after the transformation into camera spaceMore complex perspective projections require a non-affine transformation followed by an additional division to convert from 4D homogeneous space into image spaceIt is also possible to do more elaborate non-linear projections to achieve fish-eye lens effects, etc., but these require significant changes to the rendering process, not only in the vertex transformation stage, and so we will not cover them…Orthographic ProjectionAn orthographic projection is an affine transformation and so it preserves parallel linesAn example of an orthographic projection might be an architect’s top-down view of a house, or a car designer’s side view of a carThese are very valuable for certain applications, but are not used for generating realistic imagesOrthographic Projection( )−+−−+−−−+−−=⋅⋅⋅=′−1000200020002,,,,,1nearfarnearfarnearfarbottomtopbottomtopbottomtopleftrightleftrightleftrightfarneartopbottomrightleftorthoPvWCPvPerspective ProjectionWith a perspective projection, objects become smaller as they get further from the camera, as one normally gets from a photograph or video imageThe goal of most real-world camera lens makers is to achieve a perfect perspective projectionMost realistic computer generated images use perspective projectionsA fundamental property of perspective projections is that parallel lines will not necessarily remain parallel after the transformation (making them non-affine)View VolumeA camera defines some sort of 3D shape in world space that represents the volume viewable by that cameraFor example, a normal perspective camera with a rectangular image describes a sort of infinite pyramid in spaceThe tip point of the pyramid is at the camera’s origin and the pyramid projects outward in front of the camera into spaceIn computer graphics, we typically prevent this pyramid from being infinite by chopping it off at some distance from the camera. We refer to this as the far clipping planeWe also put a limit on the closest range of the pyramid by chopping off the tip. We refer to this as the near clipping planeA pyramid with the tip cut off is known as a frustum. We refer to a standard perspective view volume as a view frustumView FrustumIn a sense, we can think of this view frustrum as a distorted cube, since it has six faces, each with 4 sidesIf we think of this cube as ranging from -1 to 1 in each dimension xyz, we can think of a perspective projection as a mapping from this view frustrum into a normalized view space, or image spaceWe need a way to represent this transformation mathematicallyView FrustumThe view frustum is usually defined by a field of view (FOV) and an aspect ratio, in addition to the near and far clipping plane distancesDepending on the convention, the FOV may represent

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