PIPE: Privacy-preserving 6DoF Pose Estimation for Immersive Applications

Image-based mapping and localization offer six degrees of freedom (6DoF) pose estimation for immersive applications. This is achieved by matching, on a server, 2D visual features extracted from a mobile device's camera view and 3D features stored in a map. While effective, this process may lead to privacy breaches (e.g., exposure of sensitive information captured by camera views). To tackle this crucial issue, we present PIPE, a first-of-its-kind Privacy-preserving Image-based 6DoF Pose Estimation system. The design of PIPE is motivated by our key observation that uploading only a small amount of features extracted from camera views for pose estimation could reduce privacy leakage. However, trade-offs exist between privacy preservation, system utility (i.e., pose estimation accuracy), and system performance (e.g., end-to-end latency). To balance the trade-offs, PIPE deliberately explores the feature-detection space to reduce computation latency, designs an efficient feature ranking method by judiciously utilizing map data, and optimizes feature selection by jointly considering the features' ranking and spatial distribution to improve pose estimation accuracy. Moreover, we construct a learning-based metric to quantify the extent of privacy leakage in images. Our extensive performance evaluation reveals that PIPE can effectively preserve privacy and reduce end-to-end latency by up to 22.6%, while marginally affecting pose estimation accuracy (e.g., as low as 2.7%).