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.

翻译：社交VR平台允许用户创建和分享自己的虚拟空间，从而支持社交、经济和创意活动。在社交VR中，VR场景内的摄影是访客活动的重要指标。尽管自动识别VR场景内的拍照点可以简化VR场景的创建过程并提升访客体验，但在定量评估VR场景中拍摄的照片以及高效探索大型VR场景方面仍存在挑战。我们提出了PanoTree，一种VR场景中的自动拍照点探索器。为评估VR场景中捕获图像的美学质量，我们在社交VR平台收集的大型照片数据集上训练了一个深度评分网络，以判断人类是否可能拍摄类似照片。此外，我们提出了一种基于分层乐观优化（HOO）的搜索算法，利用评分网络提供的奖励高效探索3D VR空间。我们的用户研究表明，该评分网络在区分随机拍摄图像与人类拍摄图像方面达到了人类水平。此外，我们还展示了利用已探索拍照点的应用，例如自动缩略图生成、VR世界创建支持以及VR场景内的访客流规划。