A study of adaptive sampling-based primitive fidelity methods

The accuracy of images is frequently compromised during the processes of pattern generation and transformation, presenting challenges for existing methodologies to achieve an optimal balance between efficiency and accuracy. To address this issue, a novel primitive fidelity method utilizing adaptive sampling is introduced. This method takes into account the constituent elements of the pattern, designating the primitive as the fundamental unit of vectorization. It initializes the path through adaptive sampling, dynamically regulates the number of segments, and employs a loss function for optimization, thereby enhancing both efficiency and accuracy. Notably, this method is model-free and does not require shape primitive labels, which allows the proposed approach to break through the limitations of specific application domains and escape the challenges of collecting and generalizing vector graphic datasets. The experimental results indicate that the proposed method demonstrates good performance in terms of generation quality and operational efficiency on both public datasets and constructed datasets.