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基于VSLAM的室内场景重建与虚实遮挡的边缘优化方法

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

  • 摘要: 在增强现实环境中, 虚拟物体和真实物体的融合效果经常受到虚实遮挡的影响. 为了提升虚实遮挡效果,提出一种室内场景下基于视觉同步定位与建图(VSLAM)的三维物体稠密重建与分割的方法. 首先利用 YOLOv5s 和ORB-SLAM2检测并去除环境中的动态特征点, 只利用静态特征点构建准确的点云地图; 然后使用 OPTICS 聚类算法约束体素边缘并进行网格分割; 最后通过结合形状先验算法对分割后的点云进行预测重建, 使分割的物体边缘更加准确. 在多个数据集上检验了所提方法, 并执行动态特征点去除和虚实遮挡实验. 结果表明, 在动态场景下相比传统ORB-SLAM2, 相机的定位精度提升了92.62%, 点云的重建精度提升了35.00%, 说明该方法可以准确地定位虚拟物体和真实物体的遮挡边缘并进行分割, 同时保持形状化的重建结果, 使得虚实遮挡效果更加真实自然.

     

    Abstract: In augmented reality environment, the fusion effect of virtual and real objects is often affected by virtual-real occlusion. To improve the occlusion effect, this article proposes a method for dense reconstruction and segmentation of three-dimensional objects in indoor scenes based on VSLAM. The 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. Finally, the proposed method predicts the reconstruction of the segmented point cloud by combining the shape prior algorithm, thereby making the edges of the segmented objects more accurate. The proposed method was tested on multiple datasets, and experiments dynamic feature points removal and virtual-real occlusion were performed. The results indicate that, compared to traditional ORB-SLAM2 in dynamic scenarios, the positioning accuracy of the camera has improved by 92.62%, and the reconstruction accuracy of point clouds has improved by 35.00%. This suggests that the method can accurately locate the occlusion edges of virtual and real objects and then segment them, while maintaining a shaped reconstruction result, thus making the occlusion effects between virtual and real objects more realistic and natural.

     

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