Feature-preserving adaptive 3D scene multi-model simplification algorithm

Abstract

In a complex 3D scene, which typically contains multiple models with distinct feature differences, determining how to set appropriate simplification rates for each model so that the 3D scene maintains its characteristics well while achieving the desired simplification rate is a challenging task. To address this, this paper proposes a 3D scene simplification algorithm that integrates neural networks to accomplish this task. The algorithm is divided into two stages: the first stage extracts local and global features of each model in the 3D scene to form feature vectors, and the second stage combines neural networks to construct a multi-layer perceptron that predicts the simplification labels for each model in the 3D scene and calculates the simplification weights for each model based on these labels. Experiments have proven that this algorithm not only maintains a good appearance of the 3D scene but also has a smaller overall simplification error compared to the traditional QEM algorithm.