An invertible-NeRF test-time optimization approach tracks points in 2D and 3D across surgical videos, reporting roughly 50% higher average precision than prior optimization-based 2D trackers.
The characteristic gluing problem for the Einstein equations and applications
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abstract
In this paper we introduce the characteristic gluing problem for the Einstein vacuum equations. We present a codimension-$10$ gluing construction for characteristic initial data which are close to the Minkowski data and we show that the $10$-dimensional obstruction space consists of gauge-invariant charges which are conserved by the linearized null constraint equations. By relating these $10$ charges to the ADM energy, linear momentum, angular momentum and the center-of-mass we prove that asymptotically flat data can be characteristically glued (including the $10$ charges) to the data of a suitably chosen Kerr spacetime, obtaining as a corollary an alternative proof of the Corvino--Schoen spacelike gluing construction. Moreover, we derive a localized version of our construction where the given data restricted on an angular sector is characteristically glued to the Minkowski data restricted on another angular sector. As a corollary we obtain an alternative proof of the Carlotto-Schoen localized spacelike gluing construction. Our method yields no loss of decay in the transition region, resolving an open problem. We also discuss a number of other applications.
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Surg-InvNeRF: Invertible NeRF for 3D tracking and reconstruction in surgical vision
An invertible-NeRF test-time optimization approach tracks points in 2D and 3D across surgical videos, reporting roughly 50% higher average precision than prior optimization-based 2D trackers.