论文标题
导航的私人多党感知
Private Multiparty Perception for Navigation
论文作者
论文摘要
我们引入了一个框架，用于通过将多个摄像机连接在一起，同时保留隐私，以通过混乱的环境导航。大型环境中的遮挡和障碍通常是导航代理的挑战性情况，因为从单个相机视图中无法完全观察到环境。鉴于环境的多个摄像头视图，我们的方法学会产生只能用于导航的多视图场景表示形式，事实证明可以防止一方推断出超出输出任务的任何内容。在我们将公开发布的新导航数据集中，实验表明，私人多方表示允许在复杂的场景和障碍物周围导航，同时共同保留隐私。我们的方法缩放到任意数量的相机观点。我们认为，开发保留隐私的视觉表示形式对于许多应用程序（例如导航）越来越重要。
We introduce a framework for navigating through cluttered environments by connecting multiple cameras together while simultaneously preserving privacy. Occlusions and obstacles in large environments are often challenging situations for navigation agents because the environment is not fully observable from a single camera view. Given multiple camera views of an environment, our approach learns to produce a multiview scene representation that can only be used for navigation, provably preventing one party from inferring anything beyond the output task. On a new navigation dataset that we will publicly release, experiments show that private multiparty representations allow navigation through complex scenes and around obstacles while jointly preserving privacy. Our approach scales to an arbitrary number of camera viewpoints. We believe developing visual representations that preserve privacy is increasingly important for many applications such as navigation.
