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Near-Periodic Patterns (NPP) are ubiquitous in man-made scenes and arecomposed of tiled motifs with appearance differences caused by lighting,defects, or design elements. A good NPP representation is useful for manyapplications including image completion, segmentation, and geometric remapping.But representing NPP is challenging because it needs to maintain globalconsistency (tiled motifs layout) while preserving local variations (appearancedifferences). Methods trained on general scenes using a large dataset orsingle-image optimization struggle to satisfy these constraints, while methodsthat explicitly model periodicity are not robust to periodicity detectionerrors. To address these challenges, we learn a neural implicit representationusing a coordinate-based MLP with single image optimization. We design an inputfeature warping module and a periodicity-guided patch loss to handle bothglobal consistency and local variations. To further improve the robustness, weintroduce a periodicity proposal module to search and use multiple candidateperiodicities in our pipeline. We demonstrate the effectiveness of our methodon more than 500 images of building facades, friezes, wallpapers, ground, andMondrian patterns on single and multi-planar scenes.

Learning Continuous Implicit Representation for Near-Periodic Patterns