Image structure analysis and printing path generation technology in the conversion of digital painting to solidization

This article focuses on the key technologies involved in the transition from digital painting to materialization, with a particular emphasis on image structure analysis and printing path generation methods. With the integration of digital art and 3D printing technology, how to efficiently and accurately convert 2D digital painting into 3D entities has become a key issue. This article proposes an edge detection algorithm based on deep learning (DL) and a 3D printing path planning strategy combined with Q-learning. This method first uses DL models for image processing of digital painting, extracts image edge features through convolution and pooling operations, identifies main contour and structural information, and provides reliable basis for subsequent 3D modeling; Subsequently, Q-learning reinforcement learning (RL) mechanism was introduced to optimize the printing path, improve printing efficiency and molding quality. The results indicate that the proposed method effectively improves the efficiency and quality of entity transformation while ensuring high-precision restoration of the original painting style, providing a new technological path for the physical expression of digital art works.