Three-dimensional (3D) reconstruction of real-world scenes is a crucial task in various fields such as virtual reality, computer graphics, and urban planning. With the advancement of technology, the combination of Neural Radiance Fields (NeRF) and Unmanned Aerial Vehicles (UAVs) has gained significant attention for efficient and accurate 3D reconstruction. This paper presents a comprehensive discussion on the technical pathway for integrating NeRF with UAVs to achieve real-scene 3D reconstruction. The proposed approach leverages the capabilities of deep learning, computer vision, and aerial robotics to produce detailed 3D models of real-world environments. Mathematical formulations and algorithms are presented to demonstrate the feasibility and effectiveness of the NeRF-UAV integration in 3D reconstruction.
In order to address the high cost and limited robustness of continuous lens pose calculation in AR application systems, this paper proposes an AR registration method based on the SA-PSO algorithm. This method mainly combines the advantages of SA and PSO algorithms to fit the camera’s position and posture in a top-down manner. By continuously optimizing multiple particles in the feature space, the computational complexity of the AR registration process is reduced, and the effectiveness of registration time and robustness in practical application scenarios is ensured. The experimental results show that the proposed SA-PSO registration method can effectively achieve AR registration fusion in both manual marking and texture feature modes, and has a certain degree of robustness against occlusion.
Forest canopy structure is very important to measure forest change and forest coverage. And TerraSAR-X data are well suited for inversion applications at tree height. Based on the Random Volume over Ground model, the three-stage algorithm and its PSO improvement are studied in this paper. Taking the TerraSAR-X data of Mengla County in Yunnan Province China as the data source, the forest height inversion algorithm were compared in the experiment part. Finally, the results are verified with the field measured data. The results show that the precision of forest height inversion based on the PSO intelligent algorithm is better than the traditional three-stage algorithm, and the correlation coefficient is improved by more than 20%.
To solve the registration problem of complex texture objects in monocular images in an AR auxiliary assembly system, a 3D object registration method based on Mask RCNN edge extraction is proposed in this paper. This method mainly adopts the edge contour feature of long plate workpiece and enhances the robustness of the algorithm by constructing the matching relationship between 3D model and 2D feature. The experimental result shows that compared with the contour template matching of the Holcon, the Mask CNN-based pose estimation method proposed in this paper can effectively improve the effectiveness of the auxiliary system.
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