3D Vision Reconstruction Method Based on Adaptive Convolutional Networks in Virtual Reality

In this paper, an innovative adaptive convolutional network (ACN) architecture is proposed to address the challenge of 3D vision reconstruction in virtual reality (VR) scenarios. By dynamically adjusting the parameters and structure of the convolutional kernel, the proposed method can automatically optimize the feature extraction process according to the characteristics of the input image data. This work describes the design idea, training strategy and optimization algorithm of the network in detail, and verifies its effectiveness in VR scenarios through a large number of experiments. Experimental results show that compared with traditional methods, the proposed ACN has significant advantages in 3D reconstruction accuracy, processing speed and robustness. This method can efficiently reconstruct fine 3D models of objects in complex VR scenes, while maintaining high real-time performance, providing users with a more realistic and immersive VR experience. In addition, the flexibility of ACNs enables them to adapt to different types and complexity of VR scenarios, showing a wide range of application potential. The 3D vision reconstruction method proposed in this paper provides strong technical support for the development of VR technology.