Radiance calculation method with mixing of Kd-tree with Voronoi diagram

Abstract

The invention relates to a radiance calculation method with the mixing of a Kd-tree with a Voronoi diagram. The method comprises the steps of (1) constructing a scene topology structure and a Kd-tree hierarchical organization, (2) storing generated a photon map with a polygon face as the unit, (3) carrying out traversal of each polygon of a three-dimensional scene surface, and collecting photos belonging to each polygon and forming a two-dimensional Kd-tree structure, (4) dividing each polygon surface construction Voronoi diagram and calculating the radiation flux density of a Voronoi unit, (5) calculating the intersection point x of a ray emitted from a viewpoint and a scene, (6) according to the polygon face to which the x belongs, collecting near photons by using the two-dimensional Kd-tree structure, (7) finding the precise area covered by the collected photos according to the Voronoi diagram division, and calculating the radiance of the x point. According to the method, the geometric features of local surface photon distribution can be utilized to carry out precise radiance kernel estimation.

Description

technical field [0001] The invention belongs to the technical field of computer graphics, and in particular relates to an accurate radiance calculation method for mixing a Kd-tree (Kd-tree) and a Voronoi diagram. Background technique [0002] Highly realistic rendering technology is one of the most exciting research fields in computer graphics, and accurate and detailed global illumination simulation is especially important. Photon mapping algorithm (PhotonMapping, referred to as PM) is a global illumination algorithm based on photon maps (JensenH.W.: Global illumination using photonmaps.InRenderingTechniques96. Springer, 1996, pp.21–30), because photon mapping can Better simulation of caustics and SDS, that is, the phenomenon produced by specular reflection (refraction)-diffuse reflection-specular reflection (refraction), and the ray tracing algorithm uses Monte Carlo sampling to calculate accurately under low sampling conditions Noise is generated during the initial refle...

AI Technical Summary

Problems solved by technology

For rendering scenes with complex geometric features, photon mapping cannot guarantee that the generation of bias and noise errors can be effectively controlled during rendering

The photon mapping algorithm is a biased algorithm. The reason for the deviation is that the radiance estimation is essentially a process of calculating the photon density at point x. Since point x is an abstract point without volume and size, the area around point x can only be used approximately. Instead of the photon density at point x, the average photon density within

The indirect lighting calculation can approach the correct result when the number of photons approaches infinity, but in fact, since the number of photons emitted in the entire drawing method of photon mapping cannot reach infinity (otherwise the computer system cannot process it), the distribution in The photons in the radiance estimation area are usually limited or even sparse, which causes the radius of the estimated kernel to not approach 0 under the condition of limited photons. Intuitively, photons that are "farther" from the intersection point will also Used to calculate the radiance of the intersection point, this part of the radiance is inaccurate

In addition, there are two assumptions in the radiance calculation of the traditional photon mapping algorithm: 1. The surface of the local object where the estimated inner disk is located is a flat geometric surface, that is, the local surface near the intersection point x can completely contain the estimated inner circle 2. All photons whose distance from the intersection point x is less than r will produce an effective radiation contribution to point x (ToshiyaT., OgakiS., JensenH.W.: Progressivephotonmapping.ACMTrans.Graph.27,5(Dec.2008 ),130:1–130:8)

At the same time, in the photon mapping method, the photon map is often stored in the three-dimensional space Kd-tree (Kd-tree) hierarchical organization structure of discrete photons, which has completely discarded the geometric information of the three-dimensional scene to be drawn, and in the photon collection stage due to the The spatial geometric information around the point x is completely unknown, so the estimation calculation kernel’s assumption that there is photon distribution in the entire disk range cannot be satisfied in practice, that is, the numerator and denominator of the radiance estimation equation are both inaccurate, which leads to The wrong radiance estimate for is a bias error

The scene rendering result caused by the deviation problem is blurred, that is, local details are lost

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