Conducting a wind turbine inspection is crucial for wind-powered energy’s overall performance and longevity. However, Wind turbine inspection is a tedious and dangerous process due to the extreme height. Additionally, the maintenance of these offshore or onshore wind turbines, especially in remote areas, remains a challenging task. To address this problem, accurate 3D object rendering approaches are adopted to enable real-time monitoring of the wind turbine’s performance and condition, which requires wind turbine 3D reconstruction before modeling. In this work, a vision-based autonomous wind turbine inspection framework using a quadrotor is designed based on neural radiance fields for image-based rendering. The simulation testing results and the real-world experiment on a wind turbine model have shown the effectiveness of the proposed approach.