Figure 2. Overview of our proposed framework GenNBV. Our end-to-end policy takes the historical multi-source observations as input,
transforms them into a more informative scene representation, and produces the next viewpoint position. A reward signal will be returned
at training time to optimize the end-to-end policy for maximizing the expected cumulative reward in one episode. Specifically, the signal
is the increased coverage ratio after collecting a new viewpoint.