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:我们提出了一个叫做DroNERRF的新型优化算法,用于在实时3D重建对象周围自动定位单摄像头无人机,仅使用少量图像。神经辐射场或NeRF,是一种新颖的视觉合成技术,用于从一组输入图像中产生物体或场景的新观点。使用无人机与NERF一起提供一种独特和动态的方法,以产生对场景的新观点,特别是有限运动的场景能力有限。我们的方法侧重于计算单个无人机的优化姿势,而仅仅取决于物体的几何形状,而不使用任何外部定位系统。数据采集阶段的独特相机定位极大地影响了3D模型的质量。为了评估我们生成的新观点的质量,我们编译了不同的概念性指标,如Peak信号到噪音比(PSNR)和结构相似指数测量(SSIM)等。我们的工作展示了使用最优化的移动性有限的各种无人机定位来产生更好的效果的好处。</s>