Implicit neural representation (INR) has recently emerged as a promising
paradigm for signal representations. Typically, INR is parameterized by a
multiplayer perceptron (MLP) which takes the coordinates as the inputs and
generates corresponding attributes of a signal. However, MLP-based INRs face
two critical issues: i) individually considering each coordinate while ignoring
the connections; ii) suffering from the spectral bias thus failing to learn
high-frequency components. While target visual signals usually exhibit strong
local structures and neighborhood dependencies, and high-frequency components
are significant in these signals, the issues harm the representational capacity
of INRs. This paper proposes Conv-INR, the first INR model fully based on
convolution. Due to the inherent attributes of convolution, Conv-INR can
simultaneously consider adjacent coordinates and learn high-frequency
components effectively. Compared to existing MLP-based INRs, Conv-INR has
better representational capacity and trainability without requiring primary
function expansion. We conduct extensive experiments on four tasks, including
image fitting, CT/MRI reconstruction, and novel view synthesis, Conv-INR all
significantly surpasses existing MLP-based INRs, validating the effectiveness.
Finally, we raise three reparameterization methods that can further enhance the
performance of the vanilla Conv-INR without introducing any extra inference
cost.

本文提出了 Conv-INR，这是第一个完全基于卷积的 INR 模型，相较于现有的基于多层感知机的 INR 模型，Conv-INR 具有更好的表示能力和可训练性，同时能够有效地学习邻近坐标和高频组成部分。通过广泛的实验验证了 Conv-INR 在图像拟合、CT/MRI 重建和新视角合成等四个任务中明显优于现有的基于多层感知机的 INR 模型，而且在不引入额外推理成本的情况下，还提出了三种进一步增强 vanilla Conv-INR 性能的重新参数化方法。