  In this work, we tackle the task of estimating the 6D pose of an object from
point cloud data. While recent learning-based approaches to addressing this
task have shown great success on synthetic datasets, we have observed them to
fail in the presence of real-world data. We thus analyze the causes of these
failures, which we trace back to the difference between the feature
distributions of the source and target point clouds, and the sensitivity of the
widely-used SVD-based loss function to the range of rotation between the two
point clouds. We address the first challenge by introducing a new normalization
strategy, Match Normalization, and the second via the use of a loss function
based on the negative log likelihood of point correspondences. Our two
contributions are general and can be applied to many existing learning-based 3D
object registration frameworks, which we illustrate by implementing them in two
of them, DCP and IDAM. Our experiments on the real-scene TUD-L, LINEMOD and
Occluded-LINEMOD datasets evidence the benefits of our strategies. They allow
for the first time learning-based 3D object registration methods to achieve
meaningful results on real-world data. We therefore expect them to be key to
the future development of point cloud registration methods.
