  The goal of this paper is to estimate the 6D pose and dimensions of unseen
object instances in an RGB-D image. Contrary to "instance-level" 6D pose
estimation tasks, our problem assumes that no exact object CAD models are
available during either training or testing time. To handle different and
unseen object instances in a given category, we introduce a Normalized Object
Coordinate Space (NOCS)---a shared canonical representation for all possible
object instances within a category. Our region-based neural network is then
trained to directly infer the correspondence from observed pixels to this
shared object representation (NOCS) along with other object information such as
class label and instance mask. These predictions can be combined with the depth
map to jointly estimate the metric 6D pose and dimensions of multiple objects
in a cluttered scene. To train our network, we present a new context-aware
technique to generate large amounts of fully annotated mixed reality data. To
further improve our model and evaluate its performance on real data, we also
provide a fully annotated real-world dataset with large environment and
instance variation. Extensive experiments demonstrate that the proposed method
is able to robustly estimate the pose and size of unseen object instances in
real environments while also achieving state-of-the-art performance on standard
6D pose estimation benchmarks.
