Glass Segmentation

Glass segmentation, the task of accurately identifying glass regions in images, is a challenging computer vision problem due to glass's transparency and reflective properties, which obscure underlying scenes. Current research focuses on developing advanced deep learning models, including those employing novel architectures like Fourier convolutions, attention mechanisms (both internal/external boundary and cross-modal), and multi-scale feature fusion, to overcome these challenges and improve segmentation accuracy. These advancements are crucial for applications ranging from robotics and autonomous navigation (avoiding collisions with transparent obstacles) to building energy modeling and architectural analysis (accurate window-to-wall ratio estimation). The development of large-scale, high-quality datasets, including synthetic data generated via diffusion models, is also a significant area of focus, enabling improved model training and evaluation.