A matrix sampling-and-recovery approach for many-lights rendering

Abstract

Instead of computing on a large number of virtual point lights (VPLs), scalable many-lights rendering methods effectively simulate various illumination effects only using hundreds or thousands of representative VPLs. However, gathering illuminations from these representative VPLs, especially computing the visibility, is still a tedious and time-consuming task. In this paper, we propose a new matrix sampling-and-recovery scheme to efficiently gather illuminations by only sampling a small number of visibilities between representative VPLs and surface points. Our approach is based on the observation that the lighting matrix used in manylights rendering is of low-rank, so that it is possible to sparsely sample a small number of entries, and then numerically complete the entire matrix. We propose a three-step algorithm to explore this observation. First, we design a new VPL clustering algorithm to slice the rows and group the columns of the full lighting matrix into a number of reduced matrices, which are sampled and recovered individually. Second, we propose a novel prediction method that predicts visibility of matrix entries from sparsely and randomly sampled entries. Finally, we adapt the matrix separation technique to recover the entire reduced matrix and compute final shadings. Experimental results show that our method heavily reduces the required visibility sampling in the final gathering and achieves 3--7 times speedup compared with the state-of-the-art methods on test scenes.

Spherical Gaussian‐based Lightcuts for Glossy Interreflections

“…[HPB07] formulate the many‐light problem as a matrix sampling problem and suggests sampling a small number of rows and columns from the original, large matrix of light‐pixel pairs. Some improvements have been achieved by using the matrix slicing and sparse matrix sampling method [OP11, HWJ*15]. Nabata et al .…”

A method for estimating the errors in many-light rendering with supersampling

“…Wu and Chuang [12] proposed the VisibilityCluster algorithm, which approximates the visibilities between each cluster of VPLs and those of the shading points by estimating the averaged visibility. Huo et al [4] proposed a matrix samplingand-recovery method to efficiently gather contributions from the VPLs by sampling a small number of them. Although these methods can significantly accelerate many-light rendering, the results rely on user-specified parameters, and finding the optimal parameter values remains a challenging task.…”

“…In many-light rendering, the incident radiance at a point to be shaded (the shading point) is calculated using VPLs. Because the number of VPLs used is generally quite large, previous methods [2][3][4] have clustered VPLs to streamline the computation. These methods, however, cannot control the errors produced by clustering, resulting in noise in the rendered images.…”

Sparse representation of visual data for compression and compressed sensing

“…Wang et al [352] introduced a compressed sensing method for separating noise and features from polygonal meshes. Hue et al [366] have used compressed sensing for addressing the many-lights problem in rendering. By taking a few measurements from the lighting matrix (that contains illumination information from virtual point lights to points in a scene), the authors show that a faithful recovery of the full matrix is possible.…”