Multi-Scale Attention and Dilated Convolutional Neural Network-Based 3D Scene Reconstruction for Moving Objects

Abstract

Three-dimensional (3D) scene reconstruction for moving objects remains a challenging research topic. It is crucial to effectively capture feature representations from dynamic and complex scenarios. Consequently, this work introduces the integration of multi-scale attention and dilated convolution to create an enhanced deep-learning structure for this purpose. Therefore, this paper proposes a 3D reconstruction method for moving objects based on multi-scale attention and a dilated convolutional neural network (CNN). Specifically, a multi-scale attention algorithm framework that incorporates dilated CNNs is designed to extract multi-scale features of moving targets. The dilated CNN is incorporated to enhance the model’s perception ability and receptive field while maintaining a lightweight structure. This integrated design aims to achieve automatic learning targeted at features and scene information at different scales. By increasing the effective range of information perception and further enhancing the quality of reconstruction results, a coordinate system is established for 3D scene reconstruction of moving targets. Finally, a comparative analysis of subjective vision, visualization, and reconstruction algorithms is conducted using real-world cases. The experimental results demonstrate that the proposed method exhibits significant advantages in the 3D scene reconstruction task of moving targets compared to traditional methods.