论文标题
使用Monte Carlo渲染和DeNoing从图像中形成，光和材料分解
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising
论文作者
论文摘要
可区分渲染的最新进展已从多视图图像中对3D场景进行了高质量重建。大多数方法都依赖于简单的渲染算法：预滤波的直接照明或学习的辐照度表示。我们表明，更现实的阴影模型，结合了射线追踪和蒙特卡洛整合，大大改善了形状，材料和照明的分解。不幸的是，即使在大型样本计数下，蒙特卡洛集成也提供了巨大的噪声，这使得基于梯度的逆渲染非常具有挑战性。为了解决这个问题，我们将多重重要性采样和降解纳入新型的反向渲染管道中。这基本上可以改善收敛性，并在低样本计数下实现基于梯度的优化。我们提出了一种有效的方法，可以共同重建几何形状（显式三角形网格），材料和照明，该方法与以前的工作相比实质上改善了材料和光分离。我们认为，Denoising可以成为高质量逆渲染管道的组成部分。
Recent advances in differentiable rendering have enabled high-quality reconstruction of 3D scenes from multi-view images. Most methods rely on simple rendering algorithms: pre-filtered direct lighting or learned representations of irradiance. We show that a more realistic shading model, incorporating ray tracing and Monte Carlo integration, substantially improves decomposition into shape, materials & lighting. Unfortunately, Monte Carlo integration provides estimates with significant noise, even at large sample counts, which makes gradient-based inverse rendering very challenging. To address this, we incorporate multiple importance sampling and denoising in a novel inverse rendering pipeline. This substantially improves convergence and enables gradient-based optimization at low sample counts. We present an efficient method to jointly reconstruct geometry (explicit triangle meshes), materials, and lighting, which substantially improves material and light separation compared to previous work. We argue that denoising can become an integral part of high quality inverse rendering pipelines.
