However, point cloud registration methods struggle to achieve precise global pose estimation, whereas previous pose-free NeRFs overlook geometric consistency in global reconstruction.

We present a comprehensive study of surrogate loss functions for learning to defer.

We demonstrate that these limitations can be sidestepped by reframing the generative process as a discrete optimal control episode.

We compute the convergence rates of the RSVB approximate posterior and the corresponding optimal value.