DONE: Dynamic Neural Representation Via Hyperplane Neural ODE

Jiaxu Wang, Bo Xu, Hao Cheng, Renjing Xu

Abstract: Much progress has been made in dynamic scene reconstruction by neural rendering techniques. Even if existing methods based on Neural Radiance Field (NeRF) achieve marvelous fidelity, they suffer from either complex camera settings, dense training timestamps, or limited ability to inter- and extrapolate. In this paper, a novel paradigm is presented to address these issues. Our method, called Dynamic Ode NErf (DONE), comprises two main modules. The first one trains a deformable neural representation in a static scene to store neural features. The second module establishes a deterministic neural ordinary differential equation to model the dynamics of the scene object. We also present a new 360-degree dynamic dataset for the research of entire 360-degree dynamic scene reconstruction. Experiments visually and quantitatively illustrate the effectiveness of the proposed model.