The introduction of neural radiance fields has greatly improved the
effectiveness of view synthesis for monocular videos. However, existing
algorithms face difficulties when dealing with uncontrolled or lengthy
scenarios, and require extensive training time specific to each new scenario.
To tackle these limitations, we propose DynPoint, an algorithm designed to
facilitate the rapid synthesis of novel views for unconstrained monocular
videos. Rather than encoding the entirety of the scenario information into a
latent representation, DynPoint concentrates on predicting the explicit 3D
correspondence between neighboring frames to realize information aggregation.
Specifically, this correspondence prediction is achieved through the estimation
of consistent depth and scene flow information across frames. Subsequently, the
acquired correspondence is utilized to aggregate information from multiple
reference frames to a target frame, by constructing hierarchical neural point
clouds. The resulting framework enables swift and accurate view synthesis for
desired views of target frames. The experimental results obtained demonstrate
the considerable acceleration of training time achieved - typically an order of
magnitude - by our proposed method while yielding comparable outcomes compared
to prior approaches. Furthermore, our method exhibits strong robustness in
handling long-duration videos without learning a canonical representation of
video content.

为了解决现有算法在处理无控制或长时间场景时的困难以及需要针对每个新场景进行大量训练时间的限制，我们提出了 DynPoint 算法，用于实现无约束单目视频的快速合成新视角。该算法通过预测相邻帧之间的显式 3D 对应关系来实现信息聚合，并利用这些对应关系来从多个参考帧到目标帧中聚合信息，从而实现快速准确的目标帧视角合成。实验证明，与以往方法相比，我们的方法在加速训练时间方面取得了显著的改进，通常为数量级的提高，并且在处理长时间视频时显示出较强的鲁棒性，并无需学习视频内容的规范表示。