The performance of the method scales favorably with the complexity of the input mesh and the desired level of detail due to its efficient architecture and reliance on pre-trained models. As the complexity of the input mesh increases, the method can still generate detailed outputs quickly because it operates primarily through feed-forward networks, which are significantly faster than iterative optimization methods used in other approaches.
However, the desired level of detail can impact the computational load. For instance, while the method can handle low-poly meshes efficiently, increasing the resolution or complexity of the mesh may require more computational resources during the mesh refinement stage. The multi-view normal generation process, which generates multiple normal images, may also become more demanding as the number of views increases. Nevertheless, the method's design allows for rapid inference times, typically within seconds, even for moderately complex meshes, making it suitable for interactive applications.