The multi-plane encoding approach has been highlighted for its ability to serve as static and dynamic neural radiance fields without sacrificing generality.This approach constructs related features through projection onto learnable planes and interpolating adjacent vertices. This mechanism allows the model to learn fine-grained details rapidly and achieves outstanding performance. However, it has limitations in representing the global context of the scene, such as object shapes and dynamic motion over times when available training poses are sparse. In this work, we propose refined tensorial radiance fields that harness coordinate-based networks known for strong bias toward low-frequency signals.The coordinate-based network is responsible for capturing global context, while the multi-plane network focuses on capturing fine-grained details.We demonstrate that using residual connections effectively preserves their inherent properties.Additionally, the proposed curriculum training scheme accelerates the disentanglement of these two features. We empirically show that the proposed method outperforms others for the task with static and dynamic NeRFs using sparse inputs.In particular, we prove that excessively increasing denoising regularization for multi-plane encoding effectively eliminates artifacts; however, it can lead to artificial details that appear authentic but are not present in the data. On the other hand, we note that the proposed method does not suffer from this issue.