The multi-plane representation has been highlighted for its fast training and
inference across static and dynamic neural radiance fields. This approach
constructs relevant features via projection onto learnable grids and
interpolating adjacent vertices. However, it has limitations in capturing
low-frequency details and tends to overuse parameters for low-frequency
features due to its bias toward fine details, despite its multi-resolution
concept. This phenomenon leads to instability and inefficiency when training
poses are sparse. In this work, we propose a method that synergistically
integrates multi-plane representation with a coordinate-based network known for
strong bias toward low-frequency signals. The coordinate-based network is
responsible for capturing low-frequency details, while the multi-plane
representation focuses on capturing fine-grained details. We demonstrate that
using residual connections between them seamlessly preserves their own inherent
properties. Additionally, the proposed progressive training scheme accelerates
the disentanglement of these two features. We empirically show that the
proposed method achieves comparable results to explicit encoding with fewer
parameters, and particularly, it outperforms others for the static and dynamic
NeRFs under sparse inputs.

本研究提出一种将多平面表达与基于坐标的网络协同整合的方法，在处理稀疏输入的静态和动态神经辐射场时，实现了与显式编码相媲美的效果，且参数更少。