  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.
