  Existing object pose estimation methods commonly require a one-to-one point
matching step that forces them to be separated into two consecutive stages:
visual correspondence detection (e.g., by matching feature descriptors as part
of a perception front-end) followed by geometric alignment (e.g., by optimizing
a robust estimation objective for pointcloud registration or
perspective-n-point). Instead, we propose a matching-free probabilistic
formulation with two main benefits: i) it enables unified and concurrent
optimization of both visual correspondence and geometric alignment, and ii) it
can represent different plausible modes of the entire distribution of likely
poses. This in turn allows for a more graceful treatment of geometric
perception scenarios where establishing one-to-one matches between points is
conceptually ill-defined, such as textureless, symmetrical and/or occluded
objects and scenes where the correct pose is uncertain or there are multiple
equally valid solutions.
