The Method of Edge Optimization for Indoor Scene Reconstruction and Virtual-Real Occlusion Based on VSLAM

In an augmented reality environment, the fusion effect of virtual and real objects is often affected by virtual-real occlusion. To improve the virtual-real occlusion effect, this article proposes a method for dense reconstruction and segmentation of three-dimensional objects in indoor scenes based on VSLAM. This method first uses YOLOv5s and ORB-SLAM2 to detect and remove dynamic feature points in the environment, to build an accurate point cloud map using only static feature points. Then, the OPTICS clustering algorithm is used to constrain the voxel edges and perform mesh segmentation. The method proposed in this article predicts the reconstruction of the segmented point cloud by combining the signed distance function shape prior algorithm, thereby making the edges of the segmented objects more accurate. Finally, the proposed method is tested on multiple datasets and experiments such as dynamic feature point removal and virtual-real occlusion are conducted. The results show that the method of this article can accurately segment the occlusion edges of virtual and real objects, while maintaining the shape of the reconstruction results, making the virtual-real occlusion effect more realistic and natural.