An Environment-Adaptive Camouflage Pattern Generator
Year: 2026
Type: conference-paper
Abstract: Camouflage pattern generation is a critical task designed to enhance concealment across diverse environments. Traditional approaches primarily depend on manual design or basic image processing algorithms, lacking adaptability in complex environments. Although recent studies have employed neural networks for environmental feature extraction in camouflage pattern generation, they remain inadequate not only in addressing foreground objects interference but also in generating high-quality camouflage patterns. To overcome these limitations, we propose a novel two-stage generator that systematically (1) utilizes adversarial autoencoders with partial convolution networks to extract environment-adaptive texture maps, then (2) processes these texture maps through a style transfer network to synthesize high-quality camouflage patterns. Experimental results demonstrate the outperformance of our generator in effectively generating high-quality environment-adaptive camouflage patterns.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7202341996