  Many applications including object reconstruction, robot guidance, and scene
mapping require the registration of multiple views from a scene to generate a
complete geometric and appearance model of it. In real situations,
transformations between views are unknown an it is necessary to apply expert
inference to estimate them. In the last few years, the emergence of low-cost
depth-sensing cameras has strengthened the research on this topic, motivating a
plethora of new applications. Although they have enough resolution and accuracy
for many applications, some situations may not be solved with general
state-of-the-art registration methods due to the Signal-to-Noise ratio (SNR)
and the resolution of the data provided. The problem of working with low SNR
data, in general terms, may appear in any 3D system, then it is necessary to
propose novel solutions in this aspect. In this paper, we propose a method,
{\mu}-MAR, able to both coarse and fine register sets of 3D points provided by
low-cost depth-sensing cameras, despite it is not restricted to these sensors,
into a common coordinate system. The method is able to overcome the noisy data
problem by means of using a model-based solution of multiplane registration.
Specifically, it iteratively registers 3D markers composed by multiple planes
extracted from points of multiple views of the scene. As the markers and the
object of interest are static in the scenario, the transformations obtained for
the markers are applied to the object in order to reconstruct it. Experiments
have been performed using synthetic and real data. The synthetic data allows a
qualitative and quantitative evaluation by means of visual inspection and
Hausdorff distance respectively. The real data experiments show the performance
of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The
method has been compared to several state-of-the-art methods. The ...
