We propose a partial localization approach for cloud-based visual localization. By applying geometric lifting to the map we prevent the server from knowing the exact client pose. Still, the client can locally combine multiple partial queries into a full pose.

We present privacy preserving localization and mapping without the need for calibrated cameras.

We present the first full Structure-from-Motion pipeline from privacy preserving line features.

A novel, dynamic feature matching and pose estimation strategy tailored to multi-camera systems.