Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects, often resulting in blurry or distorted rendering. Instead of calculating a single radiance field, we propose a multi-space neural radiance field (MS-NeRF) that represents the scene using a group of feature fields in parallel sub-spaces, which leads to a better understanding of the neural network toward the existence of reflective and refractive objects. Our multi-space scheme works as an enhancement to existing NeRF methods, with only small computational overheads needed for training and inferring the extra-space outputs. We demonstrate the superiority and compatibility of our approach using three representative NeRF-based models, i.e., NeRF, Mip-NeRF, and Mip-NeRF 360. Comparisons are performed on a novelly constructed dataset consisting of 25 synthetic scenes and 7 real captured scenes with complex reflection and refraction, all having 360-degree viewpoints. Extensive experiments show that our approach significantly outperforms the existing single-space NeRF methods for rendering high-quality scenes concerned with complex light paths through mirror-like objects. Our code and dataset will be publicly available at https://zx-yin.github.io/msnerf.

我们提出了一种多空间神经辐射场（MS-NeRF），通过在并行子空间中表示场景的特征场组，以更好地理解神经网络对具有反射和折射性物体的存在的情况，从而实现了对现有NeRF方法的增强，且仅需要小的计算开销。在由25个合成场景和7个具有复杂反射和折射的真实捕捉场景组成的新颖构建数据集上进行的比较表明，我们的方法比现有的单空间NeRF方法在渲染复杂光路的镜像对象的高质量场景方面表现显著优于现有的单空间NeRF方法。