  In this paper, we address the problem of detecting unseen objects from RGB
images and estimating their poses in 3D. We propose two mobile friendly
networks: MobilePose-Base and MobilePose-Shape. The former is used when there
is only pose supervision, and the latter is for the case when shape supervision
is available, even a weak one. We revisit shape features used in previous
methods, including segmentation and coordinate map. We explain when and why
pixel-level shape supervision can improve pose estimation. Consequently, we add
shape prediction as an intermediate layer in the MobilePose-Shape, and let the
network learn pose from shape. Our models are trained on mixed real and
synthetic data, with weak and noisy shape supervision. They are ultra
lightweight that can run in real-time on modern mobile devices (e.g. 36 FPS on
Galaxy S20). Comparing with previous single-shot solutions, our method has
higher accuracy, while using a significantly smaller model (2~3% in model size
or number of parameters).
