  Social VR platforms enable social, economic, and creative activities by
allowing users to create and share their own virtual spaces. In social VR,
photography within a VR scene is an important indicator of visitors'
activities. Although automatic identification of photo spots within a VR scene
can facilitate the process of creating a VR scene and enhance the visitor
experience, there are challenges in quantitatively evaluating photos taken in
the VR scene and efficiently exploring the large VR scene. We propose PanoTree,
an automated photo-spot explorer in VR scenes. To assess the aesthetics of
images captured in VR scenes, a deep scoring network is trained on a large
dataset of photos collected by a social VR platform to determine whether humans
are likely to take similar photos. Furthermore, we propose a Hierarchical
Optimistic Optimization (HOO)-based search algorithm to efficiently explore 3D
VR spaces with the reward from the scoring network. Our user study shows that
the scoring network achieves human-level performance in distinguishing randomly
taken images from those taken by humans. In addition, we show applications
using the explored photo spots, such as automatic thumbnail generation, support
for VR world creation, and visitor flow planning within a VR scene.
