Real-Time Path Guiding Using Bounding Voxel Sampling
Haolin Lu, Wesley Chang, Trevor Hedstrom, Tzu-Mao Li
摘要
We propose a real-time path guiding method, Voxel Path Guiding (VXPG), that significantly improves fitting efficiency under limited sampling budget. Our key idea is to use a spatial irradiance voxel data structure across all shading points to guide the location of path vertices. For each frame, we first populate the voxel data structure with irradiance and geometry information. To sample from the data structure for a shading point, we need to select a voxel with high contribution to that point. To importance sample the voxels while taking visibility into consideration, we adapt techniques from offline many-lights rendering by clustering pairs of shading points and voxels. Finally, we unbiasedly sample within the selected voxel while taking the geometry inside into consideration. Our experiments show that VXPG achieves significantly lower perceptual error compared to other real-time path guiding and virtual point light methods under equal-time comparison. Furthermore, our method does not rely on temporal information, but can be used together with other temporal reuse sampling techniques such as ReSTIR to further improve sampling efficiency.
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引用它的顶会 Paper2
- Neural Importance Sampling of Many LightsPedro Figueirêdo, Qihao He, Steve Bako, Nima Khademi KalantariSIGGRAPH 2025 · 被引用 1 次
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