Since single sensor and high-density point cloud data processing have certain direct processing limitations in urban traffic scenarios, this paper proposes a 3D instance segmentation and object detection framework for urban transportation scenes based on the fusion of Lidar remote sensing technology and optical image sensing technology. Firstly, multi-source and multi-mode data pre-fusion and alignment of Lidar and camera sensor data are effectively carried out, and then a unique and innovative network of stereo regional proposal selective search-driven DAGNN is constructed. Finally, using the multi-dimensional information interaction, three-dimensional point clouds with multi-features and unique concave-convex geometric characteristics are instance over-segmented and clustered by the hypervoxel storage in the remarkable octree and growing voxels. Finally, the positioning and semantic information of significant 3D object detection in this paper are visualized by multi-dimensional mapping of the boundary box. The experimental results validate the effectiveness of the proposed framework with excellent feedback for small objects, object stacking, and object occlusion. It can be a remediable or alternative plan to a single sensor and provide an essential theoretical and application basis for remote sensing, autonomous driving, environment modeling, autonomous navigation, and path planning under the V2X intelligent network space– ground integration in the future.

With the popularity of ground and airborne three-dimensional laser scanning hardware and the development of advanced technologies for computer vision in geometrical measurement, intelligent processing of point clouds has become a hot issue in artificial intelligence. The intervisibility analysis in 3D space can use viewpoint, view distance, and elevation values and consider terrain occlusion to derive the intervisibility between two points. In this study, we first use the 3D point cloud of reflected signals from the intelligent autonomous driving vehicle’s 3D scanner to estimate the field-of-view of multi-dimensional data alignment. Then, the forced metrics of mechanical Riemann geometry are used to construct the Manifold Auxiliary Surface (MAS). With the help of the spectral analysis of the finite element topology structure constructed by the MAS, an innovative dynamic intervisibility calculation is finally realized under the geometric calculation conditions of the Mix-Planes Calculation Structure (MPCS). Different from advanced methods of global and interpolation pathway-based point clouds computing, we have removed the 99.54% high-noise background and reduced the computational complexity by 98.65%. Our computation time can reach an average processing time of 0.1044 s for one frame with a 25 fps acquisition rate of the original vision sensor. The remarkable experimental results and significant evaluations from multiple runs demonstrate that the proposed dynamic intervisibility analysis has high accuracy, strong robustness, and high efficiency. This technology can assist in terrain analysis, military guidance, and dynamic driving path planning, Simultaneous Localization And Mapping (SLAM), communication base station siting, etc., is of great significance in both theoretical technology and market applications.