# Computing Visibility. Backface Culling for General Visibility. One More Trick with Planes. BSP Trees Ray Casting Depth Buffering Quiz

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1 Computing Visibility BSP Trees Ray Casting Depth Buffering Quiz Power of Plane Equations We ve gotten a lot of mileage out of one simple equation. Basis for D outcode-clipping Basis for plane-at-a-time clipping y [ n n n d ] Basis for viewpoint back-face culling z x y z = Lecture Comp 6 Spring 5 //5 Lecture One More Trick with Planes We can develop a visibility algorithm based on evaluating plane equations. Backface Culling for General Visibility Consider the central argument of the viewpoint culling algorithm. If the eye point is within the negative half-space of a face, the face is invisible. If the eye point is within the positive half-space of a face, the face is visible. Does this algorithm work for polygons in general configurations? Well almost... it would work if there were no overlapping faces. However, notice how the overlapping facets partition each other. Suppose we build a tree of these partitions.

2 I think that I shall never see A Binary Space Partition (BSP) tree is a simple spatial data structure to construct:. Select a partitioning plane/face.. Partition the remaining planes/faces according to the side of the partitioning plane that they fall on (+ or -).. Repeat with each of the two new sets. Partitioning facets: Partitioning requires testing all facets in the active set to find if they ) lie entirely on the positive side of the partition plane, ) lie entirely on the negative side, or ) if they cross it. In the rd case of a crossing face we clip the offending face into two halves (using our planeat-a-time clipping algorithm). //5 Lecture 5 Computing Visibility with BSP trees Starting at the root of the tree.. Classify viewpoint as being in the positive or negative halfspace of our plane red. Call this routine with the other child (the one in the other half-space of our eye) +blue. Draw the current partitioning plane. Call this routine with the child in the same half-space as the eye +green -green Intuitively, at each partition, we first draw the stuff further away than the current plane, then we draw the current plane, and then we draw the closer stuff. BSP traversal is called a "hidden surface elimination" algorithm, but it doesn t really "eliminate" anything; it simply orders the drawing of primitive in a back-to-front order. //5 Lecture 6 -blue 5 Computing Visibility with BSP trees Starting at the root of the tree.. Classify viewpoint as being in the positive or negative halfspace of our plane red. Call this routine with the other child (the one in the other half-space of our eye) +blue. Draw the current partitioning plane. Call this routine with the child in the same half-space as the eye +green 5 -green Intuitively, at each partition, we first draw the stuff further away than the current 5 plane, then we draw the current plane, and then we draw the closer stuff. BSP traversal is called a "hidden surface elimination" algorithm, but it doesn t really "eliminate" anything; it simply orders the drawing of primitive in a back-to-front order. -blue BSP Tree Example Computing visibility or depth-sorting with BSP trees is both simple and fast. It resolves visibility at the primitive level. Visibility computation is independent of screen size Requires considerable preprocessing of the scene primitives Primitives must be easy to subdivide along planes Supports Constructive Solid Geometry (CSG) modeling

3 Pixel-Level Visibility Ray Casting Thus far, we ve considered visibility at the level of primitives. Now we will turn our attention to a class of algorithms that consider visibility at each pixel. Algorithm: Cast a ray from the viewpoint through each pixel to find the closest surface Rendering Loop: foreach pixel in image compute ray for pixel set depth = Z MAX foreach primitive in scene if (ray intersects primitive) then if (distance < depth) then pixel = object color depth = distance to object //5 Lecture 9 //5 Lecture Ray Casting Advantages Conceptually simple Can support CSG Can take advantage of spatial coherence in scene Can be extended to handle global illumination effects (ex: shadows and reflectance) Disadvantages Renderer must have access to entire retained model Hard to map to special-purpose hardware Visibility determination is coupled to sampling Subject to aliasing Visibility computation is a function of resolution Depth Buffering Algorithm: Render each primitive onto the screen by computing the depth at each pixel in the rasterization loop. Conditionally write the pixel if it is closer than the previous depth value Rendering Loop: set depth of all pixels to Z MAX foreach primitive in scene foreach pixel in primitive compute z at pixel if (z < depth) then pixel = object color depth pixel = z

4 Depth Buffering Advantages Primitives can be processed immediately (Hence: Immediate mode graphics APIs) Primitives can be processed in any order (Exception: primitives at same depth & transparent primitives) Well suited to H/W implementation simple control of low-level (per pixel) operations Spatial coherence Incremental evaluation of loops Good memory access pattern Disadvantages Visibility determination is coupled to sampling (Subject to aliasing) Requires a Raster-sized arrray to store depth Read-Modify-Write (Hard to make fast) Excessive over-drawing What Gets Stored in a Depth-Buffer? Recall the projection matrix was augmented to include a mapping for z values. (right + left) wx right near left right left + = (top bottom) wy y top near bottom top bottom wz + far near z far far near near far near w There are two important considerations.. The mapping is from D (Affine) to D (Projective). The mapping is linear (in general, planes map to planes) //5 Lecture //5 Lecture Computing "Z" We get the following expression for zy from our projection matrix z (far + near ) far near z = z (far near ) This mapping of z values is non-linear: Linear yet Non-Linear? What does it mean for the projection mapping to preserve planes and lines, yet, have a non-linear mapping of z values. How can this be? Consider these examples: = dx + t dy = + t dx dy = As the parameter t is varied from to, what geometric object is described? t dx + + t dy Linearity of the space is preserved, but linearity of the parameterization is not. So, suppose we have function of that maps one of these forms to one of the others. Allow this function to even change the actual values of x, y, dx, and dy, as long as this linear combination form is preserved. - Does this mapping preserve lines? What is the advantage of doing this? Why do we care?

5 Interpolating Z Preserving the linearity of the space allows us to use our plane equation method for interpolating values of z in the interior of the triangle. Monotonic Z Values We need to be careful when reading the values out of a z-buffer and interpolating them. Even though, our interpolated values of z lie on a plane, uniform differences in depth-buffer values do no correspond to a uniform differences in space. However, our z-comparisons will still work because this parameter mapping, while not linear, is monotonic. Thus, we can used our handy formula for interpolating parameters within a triangle. A A A z Az B = B B z B z area C C C z Cz //5 Lecture 7 Note that when the z values are uniformly quantized the number of discrete discernable depths is greater closer to the near plane than near the far plane. - Is this good? //5 Lecture 8 Next Time Modeling Smooth Surfaces

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