Physics-based inverse rendering enables joint optimization of shape, material, and lighting based on captured 2D images. To ensure accurate reconstruction, using a light model that closely resembles the captured environment is essential. Although the widely adopted distant environmental lighting model is adequate in many cases, we demonstrate that its inability to capture spatially varying illumination can lead to inaccurate reconstructions in many real-world inverse rendering scenarios. To address this limitation, we incorporate NeRF as a non-distant environment emitter into the inverse rendering pipeline. Additionally, we introduce an emitter importance sampling technique for NeRF to reduce the rendering variance. Through comparisons on both real and synthetic datasets, our results demonstrate that our NeRF-based emitter offers a more precise representation of scene lighting, thereby improving the accuracy of inverse rendering.

翻译：基于物理的逆渲染能够根据捕获的2D图像对形状、材质和光照进行联合优化。为确保重建的准确性，使用与捕获环境高度相似的光照模型至关重要。尽管广泛采用的远距离环境光照模型在许多情况下足够有效，但我们证明，其无法捕捉空间变化的照明，可能导致许多真实世界逆渲染场景中出现不准确的重建。为解决这一局限性，我们将NeRF作为非远距离环境发射器引入逆渲染流水线。此外，我们提出了一种针对NeRF的发射器重要性采样技术，以降低渲染方差。通过在真实和合成数据集上的比较，我们的结果表明，基于NeRF的发射器能更精确地表示场景光照，从而提高了逆渲染的精度。