SRPose: Two-view Relative Pose Estimation with Sparse Keypoints
This addresses the problem of efficient and generalizable pose estimation for visual relocalization and object tracking, though it appears incremental as it builds on existing sparse keypoint and attention-based approaches.
The paper tackles two-view relative pose estimation for camera-to-world and object-to-camera scenarios by proposing SRPose, a sparse keypoint-based framework that achieves competitive or superior accuracy and speed compared to state-of-the-art methods, with robustness to varying image sizes and camera intrinsics.
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.