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We propose to tackle the problem of multiview 2D/3D rigid registration forintervention via a Point-Of-Interest Network for Tracking and Triangulation($\text{POINT}^2$). $\text{POINT}^2$ learns to establish 2D point-to-pointcorrespondences between the pre- and intra-intervention images by tracking aset of random POIs. The 3D pose of the pre-intervention volume is thenestimated through a triangulation layer. In $\text{POINT}^2$, the unifiedframework of the POI tracker and the triangulation layer enables learninginformative 2D features and estimating 3D pose jointly. In contrast to existingapproaches, $\text{POINT}^2$ only requires a single forward-pass to achieve areliable 2D/3D registration. As the POI tracker is shift-invariant,$\text{POINT}^2$ is more robust to the initial pose of the 3D pre-interventionimage. Extensive experiments on a large-scale clinical cone-beam CT (CBCT)dataset show that the proposed $\text{POINT}^2$ method outperforms the existinglearning-based method in terms of accuracy, robustness and running time.Furthermore, when used as an initial pose estimator, our method also improvesthe robustness and speed of the state-of-the-art optimization-based approachesby ten folds.

Multiview 2D/3D Rigid Registration via a Point-Of-Interest Network for Tracking and Triangulation ($\text{POINT}^2$)