An Introduction to Ray Tracing has an extensive survey of algorithms for ray–shape intersection (Glassner 1989a). Goldstein and Nagel (1971) discussed ray–quadric intersections, and Heckbert (1984) discussed the mathematics of quadrics for graphics applications in detail, with many citations to literature in mathematics and other fields. Hanrahan (1983) described a system that automates the process of deriving a ray intersection routine for surfaces defined by implicit polynomials; his system emits C source code to perform the intersection test and normal computation for a surface described by a given equation. Mitchell (1990) showed that interval arithmetic could be applied to develop algorithms for robustly computing intersections with implicit surfaces that cannot be described by polynomials and are thus more difficult to accurately compute intersections for; more recent work in this area was done by Knoll et al. (2009). See Moore’s book (1966) for an introduction to interval arithmetic.
Other notable early papers related to ray–shape intersection include Kajiya’s (1983) work on computing intersections with surfaces of revolution and procedurally generated fractal terrains. Fournier et al.’s (1982) paper on rendering procedural stochastic models and Hart et al.’s (1989) paper on finding intersections with fractals illustrate the broad range of shape representations that can be used with ray-tracing algorithms.
Kajiya (1982) developed the first algorithm for computing intersections with parametric patches. Subsequent work on more efficient techniques for direct ray intersection with patches includes papers by Stürzlinger (1998), Martin et al. (2000), and Roth et al. (2001). Benthin et al. (2004) presented more recent results and include additional references to previous work. Ramsey et al. (2004) describe an efficient algorithm for computing intersections with bilinear patches, and Ogaki and Tokuyoshi (2011) introduce a technique for directly intersecting smooth surfaces generated from triangle meshes with per-vertex normals.
An excellent introduction to differential geometry was written by Gray (1993); Section 14.3 of his book presents the Weingarten equations.
The ray–triangle intersection test in Section 3.6 was developed by Woop et al. (2013). See Möller and Trumbore (1997) for another widely used ray–triangle intersection algorithm. A ray–quadrilateral intersection routine was developed by Lagae and Dutré (2005). Shevtsov et al. (2007a) described a highly optimized ray–triangle intersection routine for modern CPU architectures and included a number of references to other recent approaches. An interesting approach for developing a fast ray–triangle intersection routine was introduced by Kensler and Shirley (2006): they implemented a program that performed a search across the space of mathematically equivalent ray–triangle tests, automatically generating software implementations of variations and then benchmarking them. In the end, they found a more efficient ray–triangle routine than had been in use previously.
Phong and Crow (1975) first introduced the idea of interpolating per-vertex shading normals to give the appearance of smooth surfaces from polygonal meshes.
The layout of triangle meshes in memory can have a measurable impact on performance in many situations. In general, if triangles that are close together in 3D space are close together in memory, cache hit rates will be higher, and overall system performance will benefit. See Yoon et al. (2005) and Yoon and Lindstrom (2006) for algorithms for creating cache-friendly mesh layouts in memory.
The curve intersection algorithm in Section 3.7 is based on the approach developed by Nakamaru and Ohno (2002). Earlier methods for computing ray intersections with generalized cylinders are also applicable to rendering curves, though they are much less efficient (Bronsvoort and Klok 1985; de Voogt et al. 2000). The book by Farin (2001) provides an excellent general introduction to splines, and the blossoming approach used in Section 3.7 was introduced by Ramshaw (1987).
One challenge with rendering thin geometry like hair and fur is that thin geometry may require many pixel samples to be accurately resolved, which in turn increases rendering time. van Swaaij (2006) described a system that precomputed voxel grids to represent hair and fur, storing aggregate information about multiple hairs in a small region of space for more efficient rendering. More recently, Qin et al. (2014) described an approach based on cone tracing for rendering fur, where narrow cones are traced instead of rays. In turn, all of the curves that intersect a cone can be considered in computing the cone’s contribution, allowing high-quality rendering with a small number of cones per pixel.
Subdivision surfaces were invented by Doo and Sabin (1978) and Catmull and Clark (1978). The Loop subdivision method was originally developed by Charles Loop (1987), although the implementation in pbrt uses the improved rules for subdivision and tangents along boundary edges developed by Hoppe et al. (1994). There has been extensive subsequent work in subdivision surfaces. The SIGGRAPH course notes give a good summary of the state of the art in the year 2000 and also have extensive references (Zorin et al. 2000). See also Warren’s book on the topic (Warren 2002). Müller et al. (2003) described an approach that refines a subdivision surface on demand for the rays to be tested for intersection with it. (See also Benthin et al. (2007) for a related approach.)
An exciting development in subdivision surfaces is the ability to evaluate them at arbitrary points on the surface (Stam 1998). Subdivision surface implementations like the one in this chapter are often relatively inefficient, spending as much time dereferencing pointers as they do applying subdivision rules. Stam’s approach avoids these inefficiencies. Bolz and Schröder (2002) suggest an improved implementation approach that precomputes a number of quantities that make it possible to compute the final mesh much more efficiently. More recently, Patney et al. (2009) have demonstrated a very efficient approach for tessellating subdivision surfaces on data-parallel throughput processors.
Higham’s (2002) book on floating-point computation is excellent; it also develops the notation that we have used in Section 3.9. Other good references to this topic are Wilkinson (1994) and Goldberg (1991). While we have derived floating-point error bounds manually, see the Gappa system by Daumas and Melquiond (2010) for a tool that automatically derives forward error bounds of floating-point computations.
The incorrect self-intersection problem has been a known problem for ray-tracing practitioners for quite some time (Haines 1989; Amanatides and Mitchell 1990). In addition to offsetting rays by an “epsilon” at their origin, approaches that have been suggested include ignoring intersections with the object that was previously intersected, “root polishing” (Haines 1989; Woo et al. 1996), where the computed intersection point is refined to become more numerically accurate; and using higher precision floating-point representations (e.g., double instead of float).
Kalra and Barr (1989) and Dammertz and Keller (2006) developed algorithms for numerically robust intersections based on recursively subdividing object bounding boxes, discarding boxes that don’t encompass the object’s surface, and discarding boxes missed by the ray. Both of these approaches are much less efficient than traditional ray–object intersection algorithms as well as the techniques introduced in Section 3.9.
Salesin et al. (1989) introduced techniques to derive robust primitive operations for computational geometry that accounted for floating-point round-off error, and Ize showed how to perform numerically robust ray-bounding box intersections (Ize 2013); his approach is implemented in Section 3.9.2. (With a more careful derivation, he shows that a scale factor of can actually be used to increase tMax, rather than the we have derived here.) Wächter (2008) discussed self-intersection issues in his thesis; he suggested recomputing the intersection point starting from the initial intersection (root polishing) and offsetting spawned rays along the normal by a fixed small fraction of the intersection point’s magnitude. The approach implemented in this chapter uses his approach of offsetting ray origins along the normal but is based on conservative bounds on the offsets based on the numerical error present in computed intersection points. (As it turns out, our bounds are generally tighter than Wächter’s offsets while also being provably conservative.)
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