Light-weight 3D mesh generation networks based on multi-stage and progressive knowledge distillation

Due to the high data dimensionality and the complexity of the problem, existing 3D mesh reconstruction models often require significant computational resources to achieve satisfactory results. While lightweight model based on knowledge distillation has been explored in many fields such as image classification, training a lightweight 3D mesh reconstruction model remains a challenging task. In this paper, we propose a method to learn a lightweight 3D mesh reconstruction network using knowledge distillation. Specifically, we introduce a novel approach called multi-stage and progressive knowledge distillation, which effectively enhances the guidance from the teacher network to the student network, thereby improving reconstruction performance. Additionally, we propose a projection-based spatial feature unpooling method to provide more accurate spatial features for the increased spatial points. Experimental results show that our lightweight 3D mesh reconstruction network has comparable performance to existing complex models while greatly reducing the number of parameters. Specifically, our method achieves its 98.97% accuracy while reducing the number of graph neural network parameters to 71.42% of the teacher network.