Further Reading

After the introduction of the ray-tracing algorithm, an enormous amount of research was done to try to find effective ways to speed it up, primarily by developing improved ray-tracing acceleration structures. Arvo and Kirk’s chapter in An Introduction to Ray Tracing (Glassner 1989a) summarizes the state of the art as of 1989 and still provides an excellent taxonomy for categorizing different approaches to ray intersection acceleration.

Kirk and Arvo (1988) introduced the unifying principle of meta-hierarchies. They showed that by implementing acceleration data structures to conform to the same interface as is used for primitives in the scene, it’s easy to mix and match different intersection acceleration schemes. pbrt follows this model since the Aggregate inherits from the Primitive base class.

Grids

Fujimoto, Tanaka, and Iwata (1986) introduced uniform grids, a spatial subdivision approach where the scene bounds are decomposed into equally sized grid cells. More efficient grid traversal methods were described by Amanatides and Woo (1987) and Cleary and Wyvill (1988). Snyder and Barr (1987) described a number of key improvements to this approach and showed the use of grids for rendering extremely complex scenes. Hierarchical grids, where grid cells with many primitives in them are themselves refined into grids, were introduced by Jevans and Wyvill (1989). More complex techniques for hierarchical grids were developed by Cazals, Drettakis, and Puech (1995) and Klimaszewski and Sederberg (1997).

Ize et al. (2006) developed an efficient algorithm for parallel construction of grids. One of their interesting findings was that grid construction performance quickly became limited by available memory bandwidth as the number of processing cores used increased.

Choosing an optimal grid resolution is important for getting good performance from grids. A good paper on this topic is by Ize et al. (2007), who provided a solid foundation for fully automatically selecting the resolution and for deciding when to refine into subgrids when using hierarchical grids. They derived theoretical results using a number of simplifying assumptions and then showed the applicability of the results to rendering real-world scenes. Their paper also includes a good selection of pointers to previous work in this area.

Lagae and Dutré (2008a) described an innovative representation for uniform grids based on hashing that has the desirable properties that not only does each primitive have a single index into a grid cell but also each cell has only a single primitive index. They show that this representation has very low memory usage and is still quite efficient.

Hunt and Mark (2008a) showed that building grids in perspective space, where the center of projection is the camera or a light source, can make tracing rays from the camera or light substantially more efficient. Although this approach requires multiple acceleration structures, the performance benefits from multiple specialized structures for different classes of rays can be substantial. Their approach is also notable in that it is in some ways a middle ground between rasterization and ray tracing.

Bounding Volume Hierarchies

Clark (1976) first suggested using bounding volumes to cull collections of objects for standard visible-surface determination algorithms. Building on this work, Rubin and Whitted (1980) developed the first hierarchical data structures for scene representation for fast ray tracing, although their method depended on the user to define the hierarchy. Kay and Kajiya (1986) implemented one of the first practical object subdivision approaches based on bounding objects with collections of slabs. Goldsmith and Salmon (1987) described the first algorithm for automatically computing bounding volume hierarchies. Although their algorithm was based on estimating the probability of a ray intersecting a bounding volume based on the volume’s surface area, it was much less effective than modern SAH BVH approaches.

The BVHAccel implementation in this chapter is based on the construction algorithm described by Wald (2007) and Günther et al. (2007). The bounding box test is the one introduced by Williams et al. (2005). An even more efficient bounding box test that does additional precomputation in exchange for higher performance when the same ray is tested for intersection against many bounding boxes was developed by Eisemann et al. (2007); we leave implementing their method for an exercise.

The BVH traversal algorithm used in pbrt was concurrently developed by a number of researchers; see the notes by Boulos and Haines (2006) for more details and background. Another option for tree traversal is that of Kay and Kajiya (1986); they maintained a heap of nodes ordered by ray distance. On GPUs, which have relatively limited amounts of on-chip memory, maintaining a stack of to-be-visited nodes for each ray may have a prohibitive memory cost. Foley and Sugerman (2005) introduced a “stackless” kd-tree traversal algorithm that periodically backtracks and searches starting from the tree root to find the next node to visit rather than storing all nodes to visit explicitly. Laine (2010) made a number of improvements to this approach, reducing the frequency of retraversals from the tree root and applying the approach to BVHs.

A number of researchers have developed techniques for improving the quality of BVHs after construction. Yoon et al. (2007) and Kensler (2008) presented algorithms that make local adjustments to the BVH, and Kopta et al. (2012) reused BVHs over multiple frames of an animation, maintaining their quality by updating the parts that bound moving objects. See also Bittner et al. (2013), Karras and Aila (2013), and Bittner et al. (2014) for recent work in this area.

Most current methods for building BVHs are based on top-down construction of the tree, first creating the root node and then partitioning the primitives into children and continuing recursively. An alternative approach was demonstrated by Walter et al. (2008), who showed that bottom-up construction, where the leaves are created first and then agglomerated into parent nodes, is a viable option. Gu et al. (2013b) developed a much more efficient implementation of this approach and showed its suitability for parallel implementation.

One shortcoming of BVHs is that even a small number of relatively large primitives that have overlapping bounding boxes can substantially reduce the efficiency of the BVH: many of the nodes of the tree will be overlapping, solely due to the overlapping bounding boxes of geometry down at the leaves. Ernst and Greiner (2007) proposed “split clipping” as a solution; the restriction that each primitive only appears once in the tree is lifted, and the bounding boxes of large input primitives are subdivided into a set of tighter sub-bounds that are then used for tree construction. Dammertz and Keller (2008a) observed that the problematic primitives are the ones with a large amount of empty space in their bounding box relative to their surface area, so they subdivided the most egregious triangles and reported substantial performance improvements. Stich et al. (2009) developed an approach that splits primitives during BVH construction, making it possible to only split primitives when an SAH cost reduction was found. See also Popov et al.’s paper (2009) on a theoretically optimum BVH partitioning algorithm and its relationship to previous approaches, and Karras and Aila (2013) for improved criteria for deciding when to split triangles. Woop et al. (2014) developed an approach to building BVHs for long, thin geometry like hair and fur; because this sort of geometry is quite thin with respect to the volume of its bounding boxes, it normally has poor performance with most acceleration structures.

The memory requirements for BVHs can be significant. In our implementation, each node is 32 bytes. With up to 2 BVH tree nodes needed per primitive in the scene, the total overhead may be as high as 64 bytes per primitive. Cline et al. (2006) suggested a more compact representation for BVH nodes, at some expense of efficiency. First, they quantized the bounding box stored in each node using 8 or 16 bytes to encode its position with respect to the node’s parent’s bounding box. Second, they used implicit indexing, where the node i ’s children are at positions 2 i and 2 i plus 1 in the node array (assuming a 2 times branching factor). They showed substantial memory savings, with moderate performance impact. Bauszat et al. (2010) developed another space-efficient BVH representation. See also Segovia and Ernst (2010), who developed compact representations of both BVH nodes and triangle meshes.

Yoon and Manocha (2006) described algorithms for cache-efficient layout of BVHs and kd-trees and demonstrated performance improvements from using them. See also Ericson’s book (2004) for extensive discussion of this topic.

The linear BVH was introduced by Lauterbach et al. (2009). Pantaleoni and Luebke (2010) developed the HLBVH generalization, using the SAH at the upper levels of the tree. They also noted that the upper bits of the Morton coded values can be used to efficiently find clusters of primitives—both of these ideas are used in our HLBVH implementation. Garanzha et al. (2011) introduced further improvements to the HLBVH, most of them targeting GPU implementations.

Other than the HLBVH path, the BVH construction implementations in the BVHAccel here haven’t been parallelized. See Wald (2012) for an approach for high-performance parallel BVH construction using the SAH throughout.

kd-trees

Glassner (1984) introduced the use of octrees for ray intersection acceleration. Use of the kd-tree for ray tracing was first described by Kaplan (1985). Kaplan’s tree construction algorithm always split nodes down their middle; MacDonald and Booth (1990) introduced the SAH approach, estimating ray–node traversal probabilities using relative surface areas. Naylor (1993) has also written on general issues of constructing good kd-trees. Havran and Bittner (2002) revisited many of these issues and introduced useful improvements. Adding a bonus factor to the SAH for tree nodes that are completely empty, as is done in our implementation, was suggested by Hurley et al. (2002). See Havran’s Ph.D. thesis (2000) for an excellent overview of high-performance kd-construction and traversal algorithms.

Jansen (1986) first developed the efficient ray traversal algorithm for kd-trees. Arvo (1988) also investigated this problem and discussed it in a note in Ray Tracing News. Sung and Shirley (1992) described a ray traversal algorithm’s implementation for a BSP-tree accelerator; our KdTreeAccel traversal code is loosely based on theirs.

The asymptotic complexity of the kd-tree construction algorithm in pbrt is upper O left-parenthesis n log squared n right-parenthesis . Wald and Havran (2006) showed that it’s possible to build kd-trees in left-parenthesis n log n right-parenthesis time with some additional implementation complexity; they reported a 2 to 3 times speedup in construction time for typical scenes.

The best kd-trees for ray tracing are built using “perfect splits,” where the primitive being inserted into the tree is clipped to the bounds of the current node at each step. This eliminates the issue that, for example, an object’s bounding box may intersect a node’s bounding box and thus be stored in it, even though the object itself doesn’t intersect the node’s bounding box. This approach was introduced by Havran and Bittner (2002) and discussed further by Hurley et al. (2002) and Wald and Havran (2006). See also Soupikov et al. (2008). Even with perfect splits, large primitives may still be stored in many kd-tree leaves; Choi et al. (2013) suggest storing some primitives in interior nodes to address this issue.

kd-tree construction tends to be much slower than BVH construction (especially if “perfect splits” are used), so parallel construction algorithms are of particular interest. Recent work in this area includes that of Shevtsov et al. (2007b) and Choi et al. (2010), who presented efficient parallel kd-tree construction algorithms with good scalability to multiple processors.

The Surface Area Heuristic

A number of researchers have investigated improvements to the SAH since its introduction to ray tracing by MacDonald and Booth (1990). Fabianowski et al. (2009) derived a version that replaces the assumption that rays are uniformly distributed throughout space with the assumption that ray origins are uniformly distributed inside the scene’s bounding box. Hunt and Mark (2008b) introduced a new SAH that accounts for the fact that rays generally aren’t uniformly distributed but rather that many of them originate from a single point or a set of nearby points (cameras and light sources, respectively). Hunt (2008) showed how the SAH should be modified when the “mailboxing” optimization is being used, and Vinkler et al. (2012) used assumptions about the visibility of primitives to adjust their SAH cost. Ize and Hansen (2011) derived a “ray termination surface area heuristic” (RTSAH), which they use to adjust BVH traversal order for shadow rays in order to more quickly find intersections with occluders. See also Moulin et al. (2015), who adapted the SAH to account for shadow rays being occluded during kd-tree traversal.

Evaluating the SAH can be costly, particularly when many different splits or primitive partitions are being considered. One solution to this problem is to only compute it at a subset of the candidate points—for example, along the lines of the bucketing approach used in the BVHAccel in pbrt. Hurley et al. (2002) suggested this approach for building kd-trees, and Popov et al. (2006) discusses it in detail. Shevtsov et al. (2007b) introduced the improvement of binning the full extents of triangles, not just their centroids.

Hunt et al. (2006) noted that if you only have to evaluate the SAH at one point, for example, you don’t need to sort the primitives but only need to do a linear scan over them to compute primitive counts and bounding boxes at the point. They showed that approximating the SAH with a piecewise quadratic based on evaluating it at a number of individual positions and using that to choose a good split leads to effective trees. A similar approximation was used by Popov et al. (2006).

While the SAH has led to very effective kd-trees and BVHs, it has become clear that it isn’t perfect: a number of researchers have noted that it’s not unusual to encounter cases where a kd-tree or BVH with a higher SAH-estimated cost gives better performance than one with lower estimated cost. Aila et al. (2013) survey some of these results and propose two additional heuristics that help address them; one accounts for the fact that most rays start on surfaces—ray origins aren’t actually randomly distributed throughout the scene, and another accounts for SIMD divergence when multiple rays traverse the hierarchy together. While these new heuristics are effective at explaining why a given tree delivers the performance that it does, it’s not yet clear how to incorporate them into tree construction algorithms.

Other Topics in Acceleration Structures

Weghorst, Hooper, and Greenberg (1984) discussed the trade-offs of using various shapes for bounding volumes and suggested projecting objects to the screen and using a z -buffer rendering to accelerate finding intersections for camera rays.

A number of researchers have investigated the applicability of general BSP trees, where the splitting planes aren’t necessarily axis aligned, as they are with kd-trees. Kammaje and Mora (2007) built BSP trees using a preselected set of candidate splitting planes. Budge et al. (2008) developed a number of improvements to their approach, though their results only approached kd-tree performance in practice due to a slower construction stage and slower traversal than kd-trees. Ize et al. (2008) showed a BSP implementation that renders scenes faster than modern kd-trees but at the cost of extremely long construction times.

There are many techniques for traversing a collection of rays through the acceleration structure together, rather than just one at a time. This approach (“packet tracing”) is an important component of high-performance ray tracing; it’s discussed in more depth in Section 17.2.2.

Animated primitives present two challenges to ray tracers: first, renderers that try to reuse acceleration structures over multiple frames of an animation must update the acceleration structures if objects are moving. Wald et al. (2007) showed how to incrementally update BVHs in this case, and Garanzha (2009) suggested creating clusters of nearby primitives and then building BVHs of those clusters (thus lightening the load on the BVH construction algorithm). A second problem is that for primitives that are moving quickly, the bounding boxes of their full motion over the frame time may be quite large, leading to many unnecessary ray–primitive intersection tests. Notable work on this issue includes Glassner (1988), who generalized ray tracing (and an octree for acceleration) to four dimensions, adding time. More recently, Grünschloß et al. (2011) developed improvements to BVHs for moving primitives. See also Wald et al.’s (2007b) survey paper on ray tracing animated scenes.

An innovative approach to acceleration structures was suggested by Arvo and Kirk (1987), who introduced a 5D data structure that subdivided based on both 3D spatial and 2D ray directions. Another interesting approach for scenes described with triangle meshes was developed by Lagae and Dutré (2008b): they computed a constrained tetrahedralization, where all triangle faces of the model are represented in the tetrahedralization. Rays are then stepped through tetrahedra until they intersect a triangle from the scene description. This approach is still a few times slower than the state-of-the-art in kd-trees and BVHs but is an interesting new way to think about the problem.

There is an interesting middle ground between kd-trees and BVHs, where the tree node holds a splitting plane for each child rather than just a single splitting plane. For example, this refinement makes it possible to do object subdivision in a kd-tree-like acceleration structure, putting each primitive in just one subtree and allowing the subtrees to overlap, while still preserving many of the benefits of efficient kd-tree traversal. Ooi et al. (1987) first introduced this refinement to kd-trees for storing spatial data, naming it the “spatial kd-tree” (skd-tree). Skd-trees have recently been applied to ray tracing by a number of researchers, including Zachmann (2002), Woop et al. (2006), Wächter and Keller (2006), Havran et al. (2006), and Zuniga and Uhlmann (2006).

When spatial subdivision approaches like grids or kd-trees are used, primitives may overlap multiple nodes of the structure and a ray may be tested for intersection with the same primitive multiple times as it passes through the structure. Arnaldi, Priol, and Bouatouch (1987) and Amanatides and Woo (1987) developed the “mailboxing” technique to address this issue: each ray is given a unique integer identifier, and each primitive records the id of the last ray that was tested against it. If the ids match, then the intersection test is unnecessary and can be skipped.

While effective, this approach doesn’t work well with a multi-threaded ray tracer. To address this issue, Benthin (2006) suggested storing a small per-ray hash table to record ids of recently intersected primitives. Shevtsov et al. (2007a) maintained a small array of the last n intersected primitive ids and searched it linearly before performing intersection tests. Although some primitives may still be checked multiple times with both of these approaches, they usually eliminate most redundant tests.

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