NeRF as a Non-Distant Environment Emitter in Physics-based Inverse Rendering
This work addresses the need for more precise lighting models in inverse rendering for computer vision and graphics applications, representing an incremental improvement over existing methods.
The paper tackles the problem of inaccurate reconstructions in physics-based inverse rendering due to the limitations of distant environmental lighting models, and demonstrates that incorporating NeRF as a non-distant environment emitter improves accuracy in real and synthetic datasets.
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.