A survey of photon mapping state-of-the-art research and future challenges <s> Photon splatting <s> Global illumination simulates all transfers of light in a scene. The results of the simulation are then used to generate photo-realistic images. Scenes with diffuse surfaces only can be displayed in real-time using the results of radiosity methods. Images of scenes with more general surfaces are created with methods based on ray tracing but do not achieve interactive frame rates. <s> BIB001 </s> A survey of photon mapping state-of-the-art research and future challenges <s> Photon splatting <s> In this paper, we present a new image based method for computing efficiently global illumination using graphics hardware. We propose a two pass method to compute global lighting at each pixel. In the first pass, photons are traced from the light sources and their hit points are stored. Then, in the second pass, each photons hit point is splatted on the image to reconstruct the irradiance. The main advantages of our method in comparison with previous approaches is scalability. Indeed, it can be used to obtain coarse solution as well as accurate solution and is well suited for complex scenes. First, we present the density estimation framework that is the theory behind our reconstruction pass to understand how to control the accuracy with our method. Then, we show how to use graphics hardware to improve reconstruction time with some restrictions but without losing the ability to compute an accurate simulation. <s> BIB002 </s> A survey of photon mapping state-of-the-art research and future challenges <s> Photon splatting <s> We present a photon splatting technique which reduces noise and blur in the rendering of caustics. Blurring of illumination edges is an inherent problem in photon splatting, as each photon is unaware of its neighbours when being splatted. This means that the splat size is usually based on heuristics rather than knowledge of the local flux density. We use photon differentials to determine the size and shape of the splats such that we achieve adaptive anisotropic flux density estimation in photon splatting. As compared to previous work that uses photon differentials, we present the first method where no photons or beams or differentials need to be stored in a map. We also present improvements in the theory of photon differentials, which give more accurate results and a faster implementation. Our technique has good potential for GPU acceleration, and we limit the number of parameters requiring user adjustment to an overall smoothing parameter and the number of photons to be traced. <s> BIB003
The first splatting method BIB001 ) was proposed as a substitute for the k-NN searching, and it realizes the accumulation of photon contribution by rendering the kernel texture for every photon's triangle to obtain results at interactive frame rates. The scalable photon splatting method BIB002 can control the accuracy of the final results and render them by splatting each photon using a quad scaled for the visible surface identifier per pixel.  presented the photon ray splatting method. They performed direct splatting of photon rays to the generated shading points by no longer storing the photons in a map. Photon splatting can obtain high-quality results with simple search data structures and with a slight increase in computation time. Another improvement related to splatting comes from BIB003 , and this is focused on the caustic effects. The authors combined photon differentials and splatting to determine the size and shape of the splats, instead of heuristic splat size. The size of splat gives rise to adaptive anisotropic flux density estimation. The photon splatting method was proposed initially to simulate the global illumination in hardware and to gain efficiency at the cost of accuracy in density estimation. With the improvement proposed, photon ray splatting can estimate the irradiance without storing the photons in a map. What the method needs to store are the eye path hit-points. This method has better density estimation than the standard photon mapping (Fig. 9) . (a) (b) Fig. 9 Comparing photon mapping using k-NN density estimation (a) with photon ray splatting method (b) using the Cornell Box scene