  Two-view pose estimation is essential for map-free visual relocalization and
object pose tracking tasks. However, traditional matching methods suffer from
time-consuming robust estimators, while deep learning-based pose regressors
only cater to camera-to-world pose estimation, lacking generalizability to
different image sizes and camera intrinsics. In this paper, we propose SRPose,
a sparse keypoint-based framework for two-view relative pose estimation in
camera-to-world and object-to-camera scenarios. SRPose consists of a sparse
keypoint detector, an intrinsic-calibration position encoder, and promptable
prior knowledge-guided attention layers. Given two RGB images of a fixed scene
or a moving object, SRPose estimates the relative camera or 6D object pose
transformation. Extensive experiments demonstrate that SRPose achieves
competitive or superior performance compared to state-of-the-art methods in
terms of accuracy and speed, showing generalizability to both scenarios. It is
robust to different image sizes and camera intrinsics, and can be deployed with
low computing resources.
