Learning Continuous Implicit Representation for Near-Periodic Patterns
This addresses the problem of maintaining global consistency and local variations in NPP for applications like image completion and segmentation, though it is incremental as it builds on neural implicit representations with specific adaptations.
The paper tackled the challenge of representing Near-Periodic Patterns (NPP) by learning a neural implicit representation using a coordinate-based MLP with single image optimization, achieving effective results on over 500 images of various patterns like building facades and wallpapers.
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.