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.

通过引入点云动态辐射场（Point-DynRF）的新框架，该论文解决了动态辐射场几何一致性仅限于相邻输入帧的问题，并通过训练神经点云和动态辐射场的全局几何信息和体积渲染过程来优化辐射场和几何代理，从而相互补充。实验证明了该方法的有效性。