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.
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