  We present a novel method to estimate the motion matrix between overlapping
pairs of 3D views in the context of indoor scenes. We use the Manhattan world
assumption to introduce lightweight geometric constraints under the form of
planes into the problem, which reduces complexity by taking into account the
structure of the scene. In particular, we define a stochastic framework to
categorize planes as vertical or horizontal and parallel or non-parallel. We
leverage this classification to match pairs of planes in overlapping views with
point-of-view agnostic structural metrics. We propose to split the motion
computation using the classification and estimate separately the rotation and
translation of the sensor, using a quadric minimizer. We validate our approach
on a toy example and present quantitative experiments on a public RGB-D
dataset, comparing against recent state-of-the-art methods. Our evaluation
shows that planar constraints only add low computational overhead while
improving results in precision when applied after a prior coarse estimate. We
conclude by giving hints towards extensions and improvements of current
results.
