We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location $(x,y,z)$ and viewing direction $(\theta, \phi)$) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons.

中文翻译：

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NeRF：将场景表示为用于视图合成的神经辐射场

我们提出了一种方法，该方法通过使用一组稀疏的输入视图优化底层连续体积场景函数来实现合成复杂场景的新视图的最先进结果。我们的算法使用全连接（非卷积）深度网络表示场景，其输入是单个连续 5D 坐标（空间位置 $(x,y,z)$ 和观察方向 $(\theta, \phi)$ )，其输出是该空间位置的体积密度和与视图相关的发射辐射。我们通过沿着相机光线查询 5D 坐标来合成视图，并使用经典的体积渲染技术将输出颜色和密度投影到图像中。因为体积渲染是自然可微的，所以优化我们的表示所需的唯一输入是一组具有已知相机姿势的图像。我们描述了如何有效地优化神经辐射场以渲染具有复杂几何形状和外观的场景的逼真新视图，并展示了优于神经渲染和视图合成的先前工作的结果。查看合成结果最好以视频形式查看，因此我们敦促读者查看我们的补充视频以进行令人信服的比较。