Abstract It is still challenging to render directional but non‐specular reflections in complex scenes. The SG‐based (Spherical Gaussian) many‐light framework provides a scalable solution but still requires a large number of glossy virtual lights to avoid spikes as well as reduce clamping errors. Directly gathering contributions from these glossy virtual lights to each pixel in a pairwise way is very inefficient. In this paper, we propose an adaptive algorithm with tighter error bounds to efficiently compute glossy interreflections from glossy virtual lights. This approach is an extension of the Lightcuts that builds hierarchies on both lights and pixels with new error bounds and new GPU‐based traversal methods between light and pixel hierarchies. Results demonstrate that our method is able to faithfully and efficiently compute glossy interreflections in scenes with highly glossy and spatial varying reflectance. Compared with the conventional Lightcuts method, our approach generates lightcuts with only one‐fourth to one‐fifth light nodes therefore exhibits better scalability. Additionally, after being implemented on GPU, our algorithms achieve a magnitude of faster performance than the previous method.

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.