  Recent work has shown good recognition results in 3D object recognition using
3D convolutional networks. In this paper, we show that the object orientation
plays an important role in 3D recognition. More specifically, we argue that
objects induce different features in the network under rotation. Thus, we
approach the category-level classification task as a multi-task problem, in
which the network is trained to predict the pose of the object in addition to
the class label as a parallel task. We show that this yields significant
improvements in the classification results. We test our suggested architecture
on several datasets representing various 3D data sources: LiDAR data, CAD
models, and RGB-D images. We report state-of-the-art results on classification
as well as significant improvements in precision and speed over the baseline on
3D detection.
