  One core challenge in object pose estimation is to ensure accurate and robust
performance for large numbers of diverse foreground objects amidst complex
background clutter. In this work, we present a scalable framework for
accurately inferring six Degree-of-Freedom (6-DoF) pose for a large number of
object classes from single or multiple views. To learn discriminative pose
features, we integrate three new capabilities into a deep Convolutional Neural
Network (CNN): an inference scheme that combines both classification and pose
regression based on a uniform tessellation of the Special Euclidean group in
three dimensions (SE(3)), the fusion of class priors into the training process
via a tiled class map, and an additional regularization using deep supervision
with an object mask. Further, an efficient multi-view framework is formulated
to address single-view ambiguity. We show that this framework consistently
improves the performance of the single-view network. We evaluate our method on
three large-scale benchmarks: YCB-Video, JHUScene-50 and ObjectNet-3D. Our
approach achieves competitive or superior performance over the current
state-of-the-art methods.
