Abstract
Current methods for 3D reconstruction and environmental mapping frequently face challenges in achieving high precision, highlighting the need for practical and effective solutions. In response to this issue, our study introduces FlyNeRF, a system integrating Neural Radiance Fields (NeRF) with drone-based data acquisition for high-quality 3D reconstruction. Utilizing unmanned aerial vehicle (UAV) for capturing images and corresponding spatial coordinates, the obtained data is subsequently used for the initial NeRF-based 3D reconstruction of the environment. Further evaluation of the reconstruction render quality is accomplished by the image evaluation neural network developed within the scope of our system. According to the results of the image evaluation module, an autonomous algorithm determines the position for additional image capture, thereby improving the reconstruction quality. The neural network introduced for render quality assessment demonstrates an accuracy of 97%. Furthermore, our adaptive methodology enhances the overall reconstruction quality, resulting in an average improvement of 2.5 dB in Peak Signal-to-Noise Ratio (PSNR) for the 10% quantile. The FlyNeRF demonstrates promising results, offering advancements in such fields as environmental monitoring, surveillance, and digital twins, where high-fidelity 3D reconstructions are crucial.
Abstract (translated)
目前用于3D建模和环境建模的方法通常很难实现高精度，这凸显了需要实际有效的解决方案。为了应对这个问题，我们的研究引入了FlyNeRF，一种将神经辐射场（NeRF）与无人机数据采集相结合的高质量3D建模系统。利用无人机捕获图像和相关空间坐标，然后将获得的数据用于环境中的最初NeRF-based 3D建模。通过系统内图像评估神经网络进一步评估建模渲染质量。根据图像评估模块的结果，自适应算法确定附加图像捕捉的位置，从而提高建模质量。用于建模质量评估的神经网络表现出97%的准确度。此外，我们的自适应方法提高了整体建模质量，使得10%分位数上的峰值信号-噪声比（PSNR）平均提高了2.5分贝。FlyNeRF显示出鼓舞人心的结果，为环境监测、监视和数字孪生等领域提供了进步，这些领域对高保真3D建模至关重要。
URL
https://arxiv.org/abs/2404.12970
