  In this work, we address the challenging task of 3D object recognition
without the reliance on real-world 3D labeled data. Our goal is to predict the
3D shape, size, and 6D pose of objects within a single RGB-D image, operating
at the category level and eliminating the need for CAD models during inference.
While existing self-supervised methods have made strides in this field, they
often suffer from inefficiencies arising from non-end-to-end processing,
reliance on separate models for different object categories, and slow surface
extraction during the training of implicit reconstruction models; thus
hindering both the speed and real-world applicability of the 3D recognition
process. Our proposed method leverages a multi-stage training pipeline,
designed to efficiently transfer synthetic performance to the real-world
domain. This approach is achieved through a combination of 2D and 3D supervised
losses during the synthetic domain training, followed by the incorporation of
2D supervised and 3D self-supervised losses on real-world data in two
additional learning stages. By adopting this comprehensive strategy, our method
successfully overcomes the aforementioned limitations and outperforms existing
self-supervised 6D pose and size estimation baselines on the NOCS test-set with
a 16.4% absolute improvement in mAP for 6D pose estimation while running in
near real-time at 5 Hz.
