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Existing approaches to Implicit Neural Representation (INR) can beinterpreted as a global scene representation via a linear combination ofFourier bases of different frequencies. However, such universal basis functionscan limit the representation capability in local regions where a specificcomponent is unnecessary, resulting in unpleasant artifacts. To this end, weintroduce a learnable spatial mask that effectively dispatches distinct Fourierbases into respective regions. This translates into collaging Fourier patches,thus enabling an accurate representation of complex signals. Comprehensiveexperiments demonstrate the superior reconstruction quality of the proposedapproach over existing baselines across various INR tasks, including imagefitting, video representation, and 3D shape representation. Our methodoutperforms all other baselines, improving the image fitting PSNR by over 3dBand 3D reconstruction to 98.81 IoU and 0.0011 Chamfer Distance.

Learning Spatially Collaged Fourier Bases for Implicit Neural Representation