多空间神经辐射场

摘要

现有的神经辐射场（NeRF）方法存在反射物体，通常导致模糊或失真的渲染。我们提出了一种多空间神经辐射场（MS-NeRF），它不是计算单个辐射场，而是使用一组特征场在并行子空间中表示场景，这有助于神经网络更好地理解反射和折射物体的存在。我们的多空间方案作为对现有NeRF方法的增强，仅需要少量的计算开销来训练和推断额外空间的输出。我们使用三种代表性基于NeRF的模型，即NeRF、Mip-NeRF和Mip-NeRF 360，展示了我们方法的优越性和兼容性。我们在一个新构建的数据集上进行比较，该数据集包含25个合成场景和7个具有复杂反射和折射的实际捕获场景，所有这些场景都具有360度的视角。大量实验表明，我们的方法在处理通过镜面物体的复杂光线路径渲染高质量场景方面明显优于现有的单空间NeRF方法。我们的代码和数据集将公开发布在https://zx-yin.github.io/msnerf。

English

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.

多空间神经辐射场

Multi-Space Neural Radiance Fields

摘要

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