Virtual and Real Occlusion Processing Method of Monocular Visual Assembly Scene Based on ORB-SLAM3
Hanzhong Xu, Chun‐Ping Chen, Qingqing Yin, Chao Ma, Feiyan Guo · Machines · 2025
Addressing the challenge of acquiring depth information in aero-engine assembly scenes using monocular vision, which complicates mixed reality (MR) virtual and real occlusion processing, we propose an ORB-SLAM3-based monocular vision assembly scene virtual and real occlusion processing method. The method proposes optimizing ORB-SLAM3 for matching and depth point reconstruction using the MNSTF algorithm. MNSTF can solve the problems of feature point extraction and matching in weakly textured and texture-less scenes by expressing the structure and texture information of the local images. It is then proposed to densify the sparse depth map using the double-three interpolation method, and the complete depth map of the real scene is created by combining the 3D model depth information in the process model. Finally, by comparing the depth values of each pixel point in the real and virtual scene depth maps, the virtual occlusion relationship of the assembly scene is correctly displayed. Experimental validation was performed with an aero-engine piping connector assembly scenario and by comparing it with Holynski’s and Kinect’s methods. The results showed that in terms of virtual and real occlusion accuracy, the average improvement was 2.2 and 3.4 pixel points, respectively. In terms of real-time performance, the real-time frame rate of this paper’s method can reach 42.4 FPS, an improvement of 77.4% and 87.6%, respectively. This shows that the method in this paper has good performance in terms of the accuracy and timeliness of virtual and real occlusion. This study further demonstrates that the proposed method can effectively address the challenges of virtual and real occlusion processing in monocular vision within the context of mixed reality-assisted assembly processes.