  In this paper, we address the problem of 3D object instance recognition and
pose estimation of localized objects in cluttered environments using
convolutional neural networks. Inspired by the descriptor learning approach of
Wohlhart et al., we propose a method that introduces the dynamic margin in the
manifold learning triplet loss function. Such a loss function is designed to
map images of different objects under different poses to a lower-dimensional,
similarity-preserving descriptor space on which efficient nearest neighbor
search algorithms can be applied. Introducing the dynamic margin allows for
faster training times and better accuracy of the resulting low-dimensional
manifolds. Furthermore, we contribute the following: adding in-plane rotations
(ignored by the baseline method) to the training, proposing new background
noise types that help to better mimic realistic scenarios and improve accuracy
with respect to clutter, adding surface normals as another powerful image
modality representing an object surface leading to better performance than
merely depth, and finally implementing an efficient online batch generation
that allows for better variability during the training phase. We perform an
exhaustive evaluation to demonstrate the effects of our contributions.
Additionally, we assess the performance of the algorithm on the large BigBIRD
dataset to demonstrate good scalability properties of the pipeline with respect
to the number of models.
