  Near-Periodic Patterns (NPP) are ubiquitous in man-made scenes and are
composed of tiled motifs with appearance differences caused by lighting,
defects, or design elements. A good NPP representation is useful for many
applications including image completion, segmentation, and geometric remapping.
But representing NPP is challenging because it needs to maintain global
consistency (tiled motifs layout) while preserving local variations (appearance
differences). Methods trained on general scenes using a large dataset or
single-image optimization struggle to satisfy these constraints, while methods
that explicitly model periodicity are not robust to periodicity detection
errors. To address these challenges, we learn a neural implicit representation
using a coordinate-based MLP with single image optimization. We design an input
feature warping module and a periodicity-guided patch loss to handle both
global consistency and local variations. To further improve the robustness, we
introduce a periodicity proposal module to search and use multiple candidate
periodicities in our pipeline. We demonstrate the effectiveness of our method
on more than 500 images of building facades, friezes, wallpapers, ground, and
Mondrian patterns on single and multi-planar scenes.
