Векторизация изображений с помощью глубокого обученияImage vectorization using deep learning

This research focuses on developing methods for converting raster images and three-dimensional objects into vector representations using deep learning. Vectorization of objects involves finding object representations using mathematical primitives and relationships between them.To achieve this goal, the following tasks were addressed: data collection, construction of mathematical models, and development of vectorization algorithms. Data collection was performed by processing scanned images of 2D and 3D objects and generating synthetic data. Automatic algorithms using computer vision methods were developed for data cleaning and processing, along with procedures for manual data processing. These algorithms facilitate semi-automatic annotation of data, opening up the possibility to train neural networks using deep learning methods. Various neural network architectures, including convolutional neural networks and transformers, are explored to create models capable of accurately and efficiently vectorizing technical drawings and 3D point clouds. The proposed algorithms demonstrate high accuracy and efficiency in solving object vectorization tasks, with potential applications in computer vision, robotics, and data visualization.