Dynamic radiance fields have emerged as a promising approach for generating novel views from a monocular video. However, previous methods enforce the geometric consistency to dynamic radiance fields only between adjacent input frames, making it difficult to represent the global scene geometry and degenerates at the viewpoint that is spatio-temporally distant from the input camera trajectory. To solve this problem, we introduce point-based dynamic radiance fields (\textbf{Point-DynRF}), a novel framework where the global geometric information and the volume rendering process are trained by neural point clouds and dynamic radiance fields, respectively. Specifically, we reconstruct neural point clouds directly from geometric proxies and optimize both radiance fields and the geometric proxies using our proposed losses, allowing them to complement each other. We validate the effectiveness of our method with experiments on the NVIDIA Dynamic Scenes Dataset and several causally captured monocular video clips.

翻译：动态辐射场已成为从单目视频生成新视角的一种有前景的方法。然而，先前的方法仅在相邻输入帧之间对动态辐射场施加几何一致性，这使得难以表示全局场景几何，并在时空上远离输入相机轨迹的视点处退化。为解决这一问题，我们提出了基于点的动态辐射场（Point-DynRF），这是一种新颖框架，其中全局几何信息与体渲染过程分别通过神经点云和动态辐射场进行训练。具体来说，我们直接从几何代理重建神经点云，并利用所提出的损失函数同时优化辐射场和几何代理，使它们能够相互补充。通过在NVIDIA动态场景数据集和若干因果采集的单目视频片段上的实验，我们验证了方法的有效性。