Geo6D: Geometric Constraints Learning for 6D Pose Estimation

Understand

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.