A survey of photon mapping state-of-the-art research and future challenges <s> Photon tracing methods <s> The photon map provides a powerful tool for approximating the irradiance in global illumination computations independent from geometry. By presenting new importance sampling techniques, we dramatically improve the memory footprint of the photon map, simplify the caustic generation, and allow for a much faster sampling of direct illumination in complicated models as they arise in a production environment. <s> BIB001 </s> A survey of photon mapping state-of-the-art research and future challenges <s> Photon tracing methods <s> The photon map method allows efficient computation of global illumination in general scenes. Individual photon hits, generated using Monte Carlo particle tracing, are stored in the maps and form a geometry independent representation of the illumination. Two important issues with the photon map are memory requirements to store the photons and the question how many photons are needed for an accurate representation of illumination in a certain scene. In this paper we introduce a method to control the density of photon maps by storing photons selectively based on a local required density criterion. This reduces memory usage significantly since in unimportant or over-dense regions less photons are stored. Results for caustic photon maps and global photon maps representing full illumination show a decrease in number of photons of a factor of 2 to 5. The required density states how accurate the photon map should be at a certain location and determines how many photons are needed in total. We also derive such a criterion based on a novel path-importance-based first pass, taking some steps towards solving the difficult ‘how many photons’ question. <s> BIB002 </s> A survey of photon mapping state-of-the-art research and future challenges <s> Photon tracing methods <s> We present Metropolis Photon Sampling (MPS), a visual importance-driven algorithm for populating photon maps. Photon Mapping and other particle tracing algorithms fail if the photons are poorly distributed. Our approach samples light transport paths that join a light to the eye, which accounts for the viewer in the sampling process and provides information to improve photon storage. Paths are sampled with a Metropolis-Hastings algorithm that exploits coherence among important light paths. We also present a technique for including user selected paths in the sampling process without introducing bias. This allows a user to provide hints about important paths or reduce variance in specific parts of the image. We demonstrate MPS with a range of scenes and show quantitative improvements in error over standard Photon Mapping and Metropolis Light Transport. <s> BIB003 </s> A survey of photon mapping state-of-the-art research and future challenges <s> Photon tracing methods <s> We present a new adaptive photon tracing algorithm which can handle illumination settings that are considered difficult for photon tracing approaches such as outdoor scenes, close-ups of a small part of an illuminated region, and illumination coming through a small gap. The key contribution in our algorithm is the use of visibility of photon path as the importance function which ensures that our sampling algorithm focuses on paths that are visible from the given viewpoint. Our sampling algorithm builds on two recent developments in Markov chain Monte Carlo methods: adaptive Markov chain sampling and replica exchange. Using these techniques, each photon path is adaptively mutated and it explores the sampling space efficiently without being stuck at a local peak of the importance function. We have implemented this sampling approach in the progressive photon mapping algorithm which provides visibility information in a natural way when a photon path contributes to a measurement point. We demonstrate that the final algorithm is strikingly simple, yet effective at sampling photons under lighting conditions that would be difficult for existing Monte Carlo ray tracing-based methods. <s> BIB004
The improvements on photon tracing are originally proposed for standard photon mapping. Density controlling for photon maps BIB002 is a method which can store photons selectively to keep the density of photons. When the density reaches a certain level, photon flux can be distributed to neighbors. BIB001 introduced another importance driven photon map generation, in which photon paths are directed into the areas with high visual importance. BIB003 applied adaptive photon tracing to progressive photon mapping. Later, BIB004 proposed a method which is appropriate due to the visible information being obtained directly. The authors established a function about importance sampling based on the visibility of each photon path. Then they employed this important sampling for choosing light sources and direction while emitting photons. They also used adaptive Markov chain Monte Carlo methods and replica exchange to render and sample, which can adjust the sampling parameters. Fig. 14 shows the results for Sibenik cathedral using photon path visibility and making the image visually plausible with replica exchange.  proposed an adaptive method by building cumulative distribution functions on the surfaces of the scene to apply the importance photon shooting technique. The adaptive cumulative distribution function was built in each node of the surface KD-tree based on their neighbors' error values, and was used to select the reflection direction of photon when a photon hits the surface in the scene. The method can accelerate the convergence through an optimized distribution.