  Accurate static structure reconstruction and segmentation of non-stationary
objects is of vital importance for autonomous navigation applications. These
applications assume a LiDAR scan to consist of only static structures. In the
real world however, LiDAR scans consist of non-stationary dynamic structures -
moving and movable objects. Current solutions use segmentation information to
isolate and remove moving structures from LiDAR scan. This strategy fails in
several important use-cases where segmentation information is not available. In
such scenarios, moving objects and objects with high uncertainty in their
motion i.e. movable objects, may escape detection. This violates the above
assumption. We present MOVES, a novel GAN based adversarial model that segments
out moving as well as movable objects in the absence of segmentation
information. We achieve this by accurately transforming a dynamic LiDAR scan to
its corresponding static scan. This is obtained by replacing dynamic objects
and corresponding occlusions with static structures which were occluded by
dynamic objects. We leverage corresponding static-dynamic LiDAR pairs.
