A fast realtime backface culling approach


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1 A fast realtime backface culling approach Vadim Manvelyan Advisor: Dr. Norman Badler April
2 Abstract Threedimensional graphics has been an area of interest in computer science for the past 40 years. As computational speed increased, so did the importance of realtime 3D rendering systems. Nowadays, they are primarily used in CAD, training simulators and computer games. Hence, there is a massmarket demand for the ability to render increasingly complex 3D scenes. Increased power however does not mean there is enough of it to waste. 3D scenes that could be considered photorealistic are normally rendered offline, with powerful render farms taking many hours per frame. It is still important to optimize in order to increase detail in a realtime scene painlessly and get one step closer to photorealism. I tried to come up with and evaluate an efficient method of backface culling that minimizes both the required calculations and memory bandwidth, thus allowing for more complex objects in a realtime scene. 1. Related Work There does not seem to be a lot of work done on the subject matter. As far as realtime rendering goes, the only published alternative to the brute force approach is the pertriangle visibility checking [1], optimized by Levi et al [2]. Brute force approach is just that send all the triangles comprising a mesh to the graphics card and let it cull the invisible ones (backfacing or outside the field of view). Its advantages are the ease of implementation and completeness. Disadvantages include clogging the graphics subsystem memory bandwidth, increased load on the GPU to sort out the unseen faces, and, finally, traversing a large volume of system memory hence the potential for cache misses is higher. Visibility checking via dot product of the view vector and the triangle s normal is an improvement over the brute force approach [1]. The algorithm possesses both precision and completeness all (and only) the visible triangles are sent to the GPU. Disadvantages are the need to create a view vector and perform a dot product calculation on a pertriangle basis, as well as fetching every single triangle from the memory in order to do so, increasing the probability of cache misses. As the complexity of models, and thus the number of faces they are constructed of, increases, this method loses its advantages. Levi et al. improved upon this algorithm by introducing a more compact data structure. However, their approach is still O(number of triangles) [2]. 2. Technical approach The problem with all of the above is that they operate on a per triangle basis, which has the potential of choking even fast CPUs in the frontend phase. The natural next step seems to find a way to discard sets of triangles at a time. The greater percentage of triangles can be discarded per computation effort, the more efficient the algorithm would be not only there
3 will be less computation per se, there will be less memory reads as well (if we know that triangles 50 to 75 are not to be rendered, we are not going to fetch them from memory). My approach is as follows. Imagine a 3D mesh bounded in a sphere. No matter where in the scene the camera is, exactly one hemisphere will be facing it (disregard view vector for now the camera potentially can see at most half of any sphere at any time, no matter how it is oriented). If we somehow subdivide the sphere, assign the mesh s faces to the subdivisions, and calculate the visible hemisphere, all polygons belonging to the invisible back hemisphere can be discarded at once. All the polygons belonging to the visible hemisphere can be rendered, without per triangle calculations. (Occlusion culling among triangles of the same object is generally left to the video card, and no backface culling algorithm implements it). Figure 1 Therefore, subdivide the sphere into sectors via meridians and parallels just like the Earth s surface and assign sets of triangles to each sector. A triangle belongs to a set if and only if a radius parallel to its normal, intersects the sphere surface in that sector (Figure 1). So, if the spherical angular difference between meridians and parallels is 10 degrees, a normal intersecting the sphere at angular coordinates (8, 4) belongs to the first sector, and so on. A seeming issue with this approach is the fact that we draw the normals from the center of the sphere, not from actual triangles. However, for convex objects, it makes no difference. The issues with concave objects will be discussed in the corresponding section. So, algorithm proceeds as follows: it is given a mesh and an angle D, representing deviation between meridians and parallels. First, a matrix of pointers, of [180/D][360/D] size, is created. The pointers address the lists of triangles whose normals map to particular sectors. Going through the triangles in the mesh, it determines the (azimuth, zenith) spherical coordinates of the point where the normal, drawn from the center of the sphere, intersects the sphere surface. Based on those numbers, a triangle is assigned to a certain list, as discussed before.
4 When rendering: if the object is in the view frustum, a vector is drawn from the bounding sphere center to the camera s location in space. The sector through which the vector passes becomes the center of the visible hemisphere. All faces whose normals deviate no more than 90 degrees from this vector in either azimuth or zenith are to be rendered. But we already have lists of triangles sorted by sectors of the sphere they project to. We just take the sector this vector belongs to, and make it a center of a (180/D+1)by(180/D+1) render matrix. (That is, mark a square of pointers centered at this sector as to be rendered ). Creating the render matrix is O(180/D * 360/D). Note that the span is 90 degrees from the borders of the central sector, so there is a slight overcompensation in case the view vector falls right on the border of the central sector. For D = 5 deg, the overcompensation is (180/5 + 1)^2 / (180/5)^2 100% = 37^2 / 36^2 100% = 5.6%. For D = 10 deg, it s 11%. Note: that s 5.6% and 11% relative to the visible hemisphere. So in reality, with D = 10, this algorithm spends 55.5% of the time and memory bandwidth the brute force approach would take, plus the O(648) overhead. Figure 2 shows a 2D abstraction of this approach, Figure 3 shows how it works in 3D. The central sector is shown in orange, rendered sectors are blue, and discarded ones white. Figure 2 Figure 3
5 An interesting point is that in most cases, only the position of the camera relative to the bounding sphere center is relevant, not the camera s view vector. While it seems counterintuitive, rotating the camera about itself does not change the set of visible sectors. The world merely rotates about the camera location point in space, and so does the mesh, the cameratospherecenter vector, and the central sector. This does not always apply to concave objects view vector may matter in some cases. Also, there is one degenerate case where position vector alone fails to provide the correct result. See the Problems section for more information. 3. Problems The main issue is that this algorithm is incomplete. Incompleteness manifests itself in 2 cases: 1) The object is concave. Figure 4 If sectors are discarded based on the eye position only, the algorithm will not be able to process concave objects. In Figure 4, faces A and D do not project to the visible hemisphere and are discarded, even if the camera faces them. This problem would only arise if the eye is very close to the object, or inside it. There are several ways around this problem. One would be incorporating the view vector into the algorithm based on the distance to the object, relative to the bounding sphere radius. Another is to switch to a different culling method as the camera gets close (it should not introduce too much of a performance hit since the camera is so close to the object, most of the scene is occluded and not rendered anyway, so there is performance to spare). Finally, the mesh may be
6 broken up into convex subsets, each with its own center and rendermap. None were implemented or tested. 2) When the camera looks over a pole, sometimes not enough sectors are rendered. Figure 5 In Figure 5, everything works as it should (the eye is in the plane of the page). Figure 6 In Figure 6, everything to the right of the slanted red line should be rendered. However, as the camera moved far away, the central sector changed. The view vector, however, did
7 not. Because projection of a line onto an inclined sphere will get farther from the axis meridian as it moves up the latitude to the pole, the span covering the visible surface should include more sectors. Since the algorithm does not calculate this projection explicitly, yellow sectors do not get rendered. An easy workaround is corner smoothing : identify corners in the render matrix and fill them with 1 s: becomes I chose to go with this simple solution since the math required to project the hemispherebounding line onto a spherical surface (and thus determine which sectors are borderline ones) is nontrivial and timeconsuming, while my primary goal is to cut down on computation time. Corner smoothing works well, and does not introduce a huge performance hit. E.g. with 10 degree sectors, there are 648 sectors in total, and 361 are usually rendered. Adding 3 sectors per corner (there are only 2 corners they are introduced by looking over a pole of the bounding sphere) brings that number to 367 an increase of less than 2%. More than 3 sectors per corner may be added as well. However, since the problem is only evident when the eye is far away (so that the central sector is close to the equator otherwise, we get a situation like in Figure 5), ultimate precision is not needed, so extra sectors may not be necessary. Corner smoothing is O(constant). There is yet another fix. This algorithm is primarily intended for nonanimated objects (at least in the present form) e.g. buildings, terrain features, etc., and allows additional polygon budget to be spent there without a performance hit. Simply rotating the bounding sphere 90 degrees, so that its polar axis is laying on the ground, eliminates the problem of looking over the pole the terrain surface prevents it. For e.g. buildings in a strategy game, this may be an optimal solution (Figure 7). Figure 7
8 4. Performance There are two points of interest: how my approach compares to others, and what is the optimal polygon count to sector angle ratio. Table 1 shows that my approach provides notably better absolute performance than the brute force rendering. It also scales better with increasing scene complexity. The actual number of triangles/sec rendered by the brute force approach is 4,498,200 triangles in the simplest scene to 4,048,380 in the most complex one a decrease in efficiency of 10%. In contrast, my method achieves 192,780*43 = 8,289,540 triangles in the most complex scene versus 7,068,600 in the simplest scene (with 5degree sectors) an efficiency increase of 17%. It also seems that having sectors spanning less than 5 degrees will not gain any more performance. The naïve dot product approach that I implemented scored at most 20% better than brute force rendering, also suffering from dropping efficiency in complex scenes. According to Levi et al. [2] their optimized dotproduct visibility checking provides about 20% increase over the naïve dot product checking (nonsimdoptimized), which gives at Frames per Second vs. Scene Complexity Number of Triangles in the Scene Degree Sectors 10 Degree Sectors 15 Degree Sectors 30 Degree Sectors Brute Force fps Table 1 most 40% over brute force rendering. My algorithm looks very competitive, given that I did not implement any processorspecific optimizations (SIMD, etc.), with the performance increase of 57% to 105%. The camera motion through the scene was hardcoded, and while the scores from one run to another are not identical, these results are repeatable within 12%. Investigating the optimal sector angle to mesh complexity relationship, I rendered a number of spheres (I chose spheres since they produce uniform sector lists, and therefore approximate an average realworld model nicely) of varying polygon count. 6 spheres of 10,620 triangles
9 each (63,720 total triangles) and 3 spheres of 21,420 triangles each (64,260 total triangles) were rendered. The results are in Table 2. Note that the number of triangles/sector distributes as follows (approximate numbers): 10,620triangle sphere 21,420triangle sphere 5 degrees 10 degrees 15 degrees 30 degrees Performance is extremely close, virtually within the margin of error. The only conclusive win is scored by 3 heavier spheres (despite A slight disadvantage in the number of polygons) in the 5degree sectors mode. This is most likely explained by less rendermap setup overhead (3 versus 6 per frame), since with 5degree sectors, the rendermap overhead is considerable. Overall, though, it seems that closer approximation and therefore less rendered backfaces is more important for performance than longer triangle lists in the rendermap. I would recommend 510 degree sector angles for most realtime uses in the foreseeable future. Sector Angle vs. Model Polygon Count 30 deg Sector Angle 15 deg 10 deg triangle spheres triangle spheres 5 deg fps Table 2 5. Conclusion and Further work My goal was to investigate whether the approach I came up with is at all useful and deserves pursuing further. It seems so. Even in its present, quite naïve form, it provides significant performance gain over brute force rendering (or dotproduct visibility checking, for that matter). Ease of implementation is another plus. If used in computer games, it could allow for more detail where game artists usually economize e.g. terrain features, buildings, trees without incurring a dramatic performance hit.
10 What else could be done to make this algorithm faster and more versatile? 1) Make the algorithm provably complete (incorporate awareness of concave objects) 2) Modify it to allow for animated meshes 3) As it stands now, the lists referred to by the rendermap matrix contain standalone triangles. A new data structure allowing for triangle strips could speed up rendering considerably. It should be able to identify the strips in the sector (not all triangles in the sector s list have to be adjacent) and across sectors, as well as cut the multisector strips efficiently as the camera is rotated around the object and sectors get discarded seems quite nontrivial. Also, that would probably require relying on singleton vertices rather than triangles, which would bring memory advantages.
11 6. References [1] Jeff Weeks, 3D Backface Culling [2] O. Levi, R. Zohar, H. Barad, A. Klimovitski, A Compact Method for Backface Culling
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