We present a novel optimization algorithm called DroNeRF for the autonomous positioning of monocular camera drones around an object for real-time 3D reconstruction using only a few images. Neural Radiance Fields or NeRF, is a novel view synthesis technique used to generate new views of an object or scene from a set of input images. Using drones in conjunction with NeRF provides a unique and dynamic way to generate novel views of a scene, especially with limited scene capabilities of restricted movements. Our approach focuses on calculating optimized pose for individual drones while solely depending on the object geometry without using any external localization system. The unique camera positioning during the data-capturing phase significantly impacts the quality of the 3D model. To evaluate the quality of our generated novel views, we compute different perceptual metrics like the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure(SSIM). Our work demonstrates the benefit of using an optimal placement of various drones with limited mobility to generate perceptually better results.

翻译：提出一种名为DroNeRF的新型优化算法，用于实现单目相机无人机围绕目标物体的自主定位，并仅通过少量图像完成实时三维重建。神经辐射场（NeRF）是一种新颖的视图合成技术，可从一组输入图像生成物体或场景的新视角。将无人机与NeRF结合使用，为生成场景新视角提供了独特且动态的方式，尤其适用于存在运动限制的有限场景。本方法专注于在仅依赖物体几何形状且无需外部定位系统的情况下，计算单个无人机的优化位姿。数据采集阶段独特的相机定位对三维模型质量具有显著影响。为评估生成新视角的质量，我们计算了峰值信噪比（PSNR）和结构相似性指数（SSIM）等不同感知指标。本工作证明了通过优化部署多个具有有限移动能力的无人机，能够生成感知质量更优的结果。