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)).
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.
The ray–triangle intersection test in Section 6.5 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). 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.
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), Roth et al. (2001), and Benthin et al. (2006), who also included additional references to previous work. Related to this, Ogaki and Tokuyoshi (2011) introduced a technique for directly intersecting smooth surfaces generated from triangle meshes with per-vertex normals.
Ramsey et al. (2004) described an algorithm for computing intersections with bilinear patches, though double-precision computation was required for robust results. Reshetov (2019) derived a more efficient algorithm that operates in single precision; that algorithm is used in pbrt’s BilinearPatch implementation. See Akenine-Möller et al. (2018) for explanations of the algorithms used in its implementation that are related to the distance between lines.
Phong and Crow (1975) introduced the idea of interpolating per-vertex shading normals to give the appearance of smooth surfaces from polygonal meshes. The use of shading normals may cause rays reflected from a surface to be on the wrong side of the true surface; Reshetov et al. (2010) described a normal interpolation technique that avoids this problem.
The layout of triangle meshes in memory can have a measurable impact on performance. 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. Relatedly, reducing the storage required for meshes can improve performance, in addition to making it possible to render more complex scenes; see for example Lauterbach et al. (2008).
Subdivision surfaces are a widely used representation of smooth surfaces; they were invented by Doo and Sabin (1978) and Catmull and Clark (1978). Warren’s book provides a good introduction to them (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, and Benthin et al. (2007, 2015) described a related approach. A more memory-efficient approach was described by Tejima et al. (2015), who converted subdivision surfaces to Bézier patches and intersected rays with those. Previous editions of this book included a section in this chapter on the implementation of subdivision surfaces, which may also be of interest.
The curve intersection algorithm in Section 6.7 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). Binder and Keller (2018) improved the recursive culling of curve intersections using cylinders to bound the curve in place of axis-aligned bounding boxes. Their approach is better suited for GPUs than the current implementation in the Curve shape, as it uses a compact bit field to record work to be done, in place of recursive evaluation.
More efficient intersection algorithms for curves have recently been developed by Reshetov (2017) and Reshetov and Luebke (2018). Related is a tube primitive described by a poly-line with a specified radius at each vertex that Han et al. (2019) provided an efficient intersection routine for.
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. One approach to this problem was described by Qin et al. (2014), who used cone tracing for rendering fur, where narrow cones are traced instead of rays. In turn, all 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.
An excellent introduction to differential geometry was written by Gray (1993); Section 14.3 of his book presents the Weingarten equations.
Higham’s (2002) book on floating-point computation is excellent; it also develops the notation that we have used in Section 6.8. 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 Herbgrind (Sanchez-Stern et al. 2018) system implements an interesting approach, automatically finding floating-point computations that suffer from excessive error during the course of a program’s execution.
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 do not 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 6.8.
Ize showed how to perform numerically robust ray–bounding box intersections (Ize 2013); his approach is implemented in Section 6.8.2. (With a more careful derivation, he showed that a scale factor of can be used to increase tMax, rather than the we derived.) 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 uses 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.)
Geometric accuracy has seen much more attention in computational geometry than in rendering. Examples include Salesin et al. (1989), who introduced techniques to derive robust primitive operations for computational geometry that accounted for floating-point round-off error, and Shewchuk (1997), who applied adaptive-precision floating-point arithmetic to geometric predicates, using just enough precision to compute a correct result for given input values.
The precision requirements of ray tracing have implications beyond practical implementation, which has been our focus. Reif et al. (1994) showed how to construct Turing machines based entirely on ray tracing and the geometric optics, which implies that ray tracing is undecidable in the sense of complexity theory. Yet in practice, optical computing systems can be constructed, though they are not able to solve undecidable problems. Blakey (2012) showed that this can be explained by careful consideration of such optical Turing machines’ precision requirements, which can grow exponentially.
Turk (1990) described two approaches for uniformly sampling the surface area of triangles. The approach implemented in SampleUniformTriangle(), which is more efficient and better preserves sample stratification than the algorithms given by Turk, is due to Talbot (2011) and Heitz (2019). Shirley et al. (1996) derived methods for sampling a number of other shapes, and Arvo and Novins (2007) showed how to sample convex quadrilaterals.
The aforementioned approaches are all based on warping samples from the unit square to the surface of the shape; an interesting alternative was given by Basu and Owen (2015, 2017), who showed how to recursively decompose triangles and disks to directly generate low-discrepancy points on their surfaces. Marques et al. (2013) showed how to generate low-discrepancy samples directly on the unit sphere; see also Christensen’s report (2018), which shows an error reduction from imposing structure on the distribution of multiple sample points on disk light sources.
Uniformly sampling the visible area of a shape from a reference point is an improvement to uniform area sampling for direct lighting calculations. Gardner et al. (1987) and Zimmerman (1995) derived methods to do so for cylinders, and Wang et al. (2006) found an algorithm to sample the visible area of cones. (For planar shapes like triangles, the visible area is trivially the entire area.)
Uniform solid angle sampling of shapes has also seen attention by a number of researchers. Wang (1992) introduced an approach for solid angle sampling of spheres. Arvo showed how to sample the projection of a triangle on the sphere of directions with respect to a reference point (Arvo 1995b); his approach is implemented in SampleSphericalTriangle(). (A more efficient approach to solid angle sampling of triangles was recently developed by Peters (2021b, Section 5).) Ureña et al. (2013) and Pekelis and Hery (2014) developed analogous techniques for sampling quadrilateral light sources; Ureña et al.’s method is used in SampleSphericalRectangle(). (To better understand these techniques for sampling projected polygons, Donnay’s book on spherical trigonometry provides helpful background (Donnay 1945).) The approach implemented in Section 6.2.4 to convert an angle in a cone to a point on a sphere was derived by Akalin (2015).
Gamito (2016) presented an approach for uniform solid angle sampling of disk and cylindrical lights based on bounding the solid angle they subtend in order to fit a quadrilateral, which is then sampled using Ureña et al.’s method (2013). Samples that do not correspond to points on the light source are rejected. A related approach was developed by Tsai et al. (2006), who approximate shapes with collections of triangles that are then sampled by solid angle. Guillén et al. (2017) subsequently developed an algorithm for directly sampling disks by solid angle that avoids rejection sampling.
Spheres are the only shapes for which we are aware of algorithms for direct sampling of their projected solid angle. An algorithm to do so was presented by Ureña and Georgiev (2018). Peters and Dachsbacher developed a more efficient approach (2019) and Peters (2019) described how to use this method to compute the PDF associated with a direction so that it can be used with multiple importance sampling.
A variety of additional techniques for projected solid angle sampling have been developed. Arvo (2001a) described a general framework for deriving sampling algorithms and showed its application to projected solid angle sampling of triangles, though numeric inversion of the associated CDF is required. Ureña (2000) approximated projected solid angle sampling of triangles by progressively decomposing them into smaller triangles until solid angle sampling is effectively equivalent. The approach based on warping uniform samples to approximate projected solid angle sampling that we implemented for triangles and quadrilateral bilinear patches was described by Hart et al. (2020). Peters (2021b) has recently shown how to efficiently and accurately perform projected solid angle sampling of polygons.
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