Existing approaches to Implicit Neural Representation (INR) can be
interpreted as a global scene representation via a linear combination of
Fourier bases of different frequencies. However, such universal basis functions
can limit the representation capability in local regions where a specific
component is unnecessary, resulting in unpleasant artifacts. To this end, we
introduce a learnable spatial mask that effectively dispatches distinct Fourier
bases into respective regions. This translates into collaging Fourier patches,
thus enabling an accurate representation of complex signals. Comprehensive
experiments demonstrate the superior reconstruction quality of the proposed
approach over existing baselines across various INR tasks, including image
fitting, video representation, and 3D shape representation. Our method
outperforms all other baselines, improving the image fitting PSNR by over 3dB
and 3D reconstruction to 98.81 IoU and 0.0011 Chamfer Distance.

通过引入可学习的空间掩码，将不同频率的傅里叶基函数有效地分配给各个区域，从而实现傅里叶拼贴，并能对复杂信号进行准确表示，该方法在各种 INR 任务中的实验表明，相较于现有基线模型，其重构质量更高，如图像拟合 PSNR 提高了 3dB 以上，3D 重建达到了 98.81 的 IoU 和 0.0011 的 Chamfer 距离。