  Manually selecting viewpoints or using commonly available flight planners
like circular path for large-scale 3D reconstruction using drones often results
in incomplete 3D models. Recent works have relied on hand-engineered heuristics
such as information gain to select the Next-Best Views. In this work, we
present a learning-based algorithm called Scan-RL to learn a Next-Best View
(NBV) Policy. To train and evaluate the agent, we created Houses3K, a dataset
of 3D house models. Our experiments show that using Scan-RL, the agent can scan
houses with fewer number of steps and a shorter distance compared to our
baseline circular path. Experimental results also demonstrate that a single NBV
policy can be used to scan multiple houses including those that were not seen
during training. The link to Scan-RL is available at
https://github.com/darylperalta/ScanRL and Houses3K dataset can be found at
https://github.com/darylperalta/Houses3K.
