Recently, Neural Radiance Fields (NeRF) is revolutionizing the task of novel
view synthesis (NVS) for its superior performance. In this paper, we propose to
synthesize dynamic scenes. Extending the methods for static scenes to dynamic
scenes is not straightforward as both the scene geometry and appearance change
over time, especially under monocular setup. Also, the existing dynamic NeRF
methods generally require a lengthy per-scene training procedure, where
multi-layer perceptrons (MLP) are fitted to model both motions and radiance. In
this paper, built on top of the recent advances in voxel-grid optimization, we
propose a fast deformable radiance field method to handle dynamic scenes. Our
method consists of two modules. The first module adopts a deformation grid to
store 3D dynamic features, and a light-weight MLP for decoding the deformation
that maps a 3D point in the observation space to the canonical space using the
interpolated features. The second module contains a density and a color grid to
model the geometry and density of the scene. The occlusion is explicitly
modeled to further improve the rendering quality. Experimental results show
that our method achieves comparable performance to D-NeRF using only 20 minutes
for training, which is more than 70x faster than D-NeRF, clearly demonstrating
the efficiency of our proposed method.

本文提出了一种基于先进的体素网格优化的快速变形辐射场方法来处理动态场景，该方法包括两个模块，采用变形网格存储动态特征和密度和色彩网格来模拟场景几何和密度，并将遮挡进行显式建模以进一步提高渲染质量。实验结果表明，本方法在仅用 20 分钟的训练时间下，达到了与 D-NeRF 相当的性能，比 D-NeRF 快 70 倍以上，显示了该方法的高效性。