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.
翻译:近视模式(NPP)在人为的场景中普遍存在,由因照明、缺陷或设计要素造成的外观差异而呈现出来的平板模型组成。良好的NPP代表对于许多应用程序都有用,包括图像完成、分解和几何重新映射。但代表NPP具有挑战性,因为它需要保持全球一致性(使用motifs布局),同时保存地方差异(出现差异) 。在一般场上培训的方法,使用大型数据集或单一图像优化来努力满足这些限制,而明确模型周期性的方法对于定期检测错误并不强健。为了应对这些挑战,我们利用基于协调的 MLP 学习了神经隐含的表达方式,同时优化了单一图像。我们设计了一个输入特征的扭曲模块和周期性引导的补丁损失,以便处理全球一致性和地方差异。为了进一步提高稳健性,我们引入了一个周期性建议模块,以搜索和使用我们管道中的多个候选周期性。我们展示了在500多幅的建筑墙壁画、折纸、地面和蒙德里安图像中所使用的方法的有效性。