  Numerous 6D pose estimation methods have been proposed that employ end-to-end
regression to directly estimate the target pose parameters. Since the visible
features of objects are implicitly influenced by their poses, the network
allows inferring the pose by analyzing the differences in features in the
visible region. However, due to the unpredictable and unrestricted range of
pose variations, the implicitly learned visible feature-pose constraints are
insufficiently covered by the training samples, making the network vulnerable
to unseen object poses. To tackle these challenges, we proposed a novel
geometric constraints learning approach called Geo6D for direct regression 6D
pose estimation methods. It introduces a pose transformation formula expressed
in relative offset representation, which is leveraged as geometric constraints
to reconstruct the input and output targets of the network. These reconstructed
data enable the network to estimate the pose based on explicit geometric
constraints and relative offset representation mitigates the issue of the pose
distribution gap. Extensive experimental results show that when equipped with
Geo6D, the direct 6D methods achieve state-of-the-art performance on multiple
datasets and demonstrate significant effectiveness, even with only 10% amount
of data.
