Is Vanilla MLP in Neural Radiance Field Enough for Few-shot View
  Synthesis?
Hanxin Zhu Tianyu He Xin Li Bingchen Li Zhibo Chen
Neural Radiance Field (NeRF) has achieved superior performance for novel view
synthesis by modeling the scene with a Multi-Layer Perception (MLP) and a
volume rendering procedure, however, when fewer known views are given (i.e.,
few-shot view synthesis), the model is prone to overfit the given views. To
handle this issue, previous efforts have been made towards leveraging learned
priors or introducing additional regularizations. In contrast, in this paper,
we for the first time provide an orthogonal method from the perspective of
network structure. Given the observation that trivially reducing the number of
model parameters alleviates the overfitting issue, but at the cost of missing
details, we propose the multi-input MLP (mi-MLP) that incorporates the inputs
(i.e., location and viewing direction) of the vanilla MLP into each layer to
prevent the overfitting issue without harming detailed synthesis. To further
reduce the artifacts, we propose to model colors and volume density separately
and present two regularization terms. Extensive experiments on multiple
datasets demonstrate that: 1) although the proposed mi-MLP is easy to
implement, it is surprisingly effective as it boosts the PSNR of the baseline
from $14.73$ to $24.23$. 2) the overall framework achieves state-of-the-art
results on a wide range of benchmarks. We will release the code upon
publication.