提出重参数化方法，高效精准采样神经BRDF，提升渲染真实感。
Neural BRDF Importance Sampling by Reparameterization
- 通过重参数化将采样问题转化为积分替换，无需反向网络
- 相比基线方法方差更低，且推理速度更快
- 适合需要高质量渲染的图形学研究与工业应用
神经双向反射分布函数（BRDF）已成为提升物理渲染真实感的流行材质表示。然而其重要性采样仍是重大挑战。本文提出一种基于重参数化的神经BRDF重要性采样方法，可无缝集成到标准渲染流程中，实现精确的BRDF样本生成。该方法将分布学习转化为BRDF积分替换问题。与依赖可逆网络和多步推断的先前方法不同，本模型消除了这些限制，具有更高灵活性与效率。方差与性能分析表明，该重参数化方法在神经BRDF渲染中实现了最佳方差降低，同时保持了高推理速度。
原文摘要 · Abstract (English)
Neural bidirectional reflectance distribution functions (BRDFs) have emerged as popular material representations for enhancing realism in physically-based rendering. Yet their importance sampling remains a significant challenge. In this paper, we introduce a reparameterization-based formulation of neural BRDF importance sampling that seamlessly integrates into the standard rendering pipeline with precise generation of BRDF samples. The reparameterization-based formulation transfers the distribution learning task to a problem of identifying BRDF integral substitutions. In contrast to previous methods that rely on invertible networks and multi-step inference to reconstruct BRDF distributions, our model removes these constraints, which offers greater flexibility and efficiency. Our variance and performance analysis demonstrates that our reparameterization method achieves the best variance reduction in neural BRDF renderings while maintaining high inference speeds compared to existing baselines.
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