Xu, TianchenYang, JialeQin, YimingSheng, BinWu, EnhuaChen, RenjieRitschel, TobiasWhiting, Emily2024-10-132024-10-132024978-3-03868-250-9https://doi.org/10.2312/pg.20241302https://diglib.eg.org/handle/10.2312/pg20241302Edge-preserving filters, as known as bilateral filters, are fundamental to graphics rendering techniques, providing greater generality and capability of edge preservation than pure convolution filters. However, sampling with a large kernel per pixel for these filters can be computationally intensive in real-time rendering. Existing acceleration methods for approximating edgepreserving filters still struggle to balance blur controllability, edge clarity, and runtime efficiency. In this paper, we propose a novel scheme for approximating edge-preserving filters with large anisotropic kernels by recursively reconstructing them from multi-image pyramid (MIP) layers that are weightedly filtered in a dual 3×3 kernel space. Our approach introduces a concise unified processing pipeline independent of kernel size, which includes upsampling and downsampling on MIP layers and enables the integration of custom edge-stopping functions. We also derive the implicit relations of the sampling weights and formulate a weight template model for inference. Furthermore, we convert the pipeline into a lightweight neural network for numerical solutions through data training. Consequently, our image post-processors achieve high-quality and high-performance edgepreserving filters in real-time, using the same control parameters as the original bilateral filters. These filters are applicable for depth-of-fields, global illumination denoising, and screen-space particle rendering. The simplicity of the reconstruction process in our pipeline makes it user-friendly and cost-effective, saving both runtime and implementation costs.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Rendering; VisibilityComputing methodologies → RenderingVisibility10.2312/pg.2024130212 pages