Category-Agnostic 6D Pose Estimation with Conditional Neural Processes

Understand

We present a novel meta-learning approach for 6D pose estimation on unknown objects.

• In contrast to ``instance-level" and ``category-level" pose estimation methods, our algorithm learns object representation in a category-agnostic way, which endows it with strong generalization capabilities across object categories.
• Specifically, we employ a neural process-based meta-learning approach to train an encoder to capture texture and geometry of an object in a latent representation, based on very few RGB-D images and ground-truth keypoints.
• The latent representation is then used by a simultaneously meta-trained decoder to predict the 6D pose of the object in new images.