Huang, XingchangMemari, PooranSeidel, Hans-PeterSingh, GurpritGhosh, AbhijeetWei, Li-Yi2022-07-012022-07-0120221467-8659https://doi.org/10.1111/cgf.14596https://diglib.eg.org:443/handle/10.1111/cgf14596Point pattern synthesis requires capturing both local and non-local correlations from a given exemplar. Recent works employ deep hierarchical representations from VGG-19 [SZ15] convolutional network to capture the features for both point-pattern and texture synthesis. In this work, we develop a simplified optimization pipeline that uses more traditional Gabor transform-based features. These features when convolved with simple random filters gives highly expressive feature maps. The resulting framework requires significantly less feature maps compared to VGG-19-based methods [TLH19; RGF*20], better captures both the local and non-local structures, does not require any specific data set training and can easily extend to handle multi-class and multi-attribute point patterns, e.g., disk and other element distributions. To validate our pipeline, we perform qualitative and quantitative analysis on a large variety of point patterns to demonstrate the effectiveness of our approach. Finally, to better understand the impact of random filters, we include a spectral analysis using filters with different frequency bandwidths.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies --> Point pattern synthesis; Point-based texture synthesisComputing methodologiesPoint pattern synthesisPoint based texture synthesis10.1111/cgf.14596169-17911 pages