The proposed method's impact extends beyond view synthesis to various applications in computer vision and robotics. By incorporating uncertainty into depth-guided NeRF training, the model can better understand the underlying scene geometry, leading to more accurate 3D reconstructions. This improved geometric understanding can benefit tasks like object recognition, scene segmentation, and robotic navigation. For instance, in robotics, a robot equipped with a neural radiance field trained using this method could navigate complex indoor environments more effectively by accurately perceiving obstacles and objects in its surroundings.