We present a novel meta-learning approach for 6D pose estimation on unknown
objects. In contrast to "instance-level" pose estimation methods, our algorithm
learns object representation in a category-agnostic way, which endows it with
strong generalization capabilities within and across object categories.
Specifically, we employ a conditional 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. To
evaluate our algorithm, experiments are conducted on our new fully-annotated
synthetic datasets generated from Multiple Categories in Multiple Scenes
(MCMS). Experimental results demonstrate that our model performs well on unseen
objects with various shapes and appearances.

本研究提出了一种新颖的基于元学习的方法，用于对未知对象进行六自由度姿态估计，并通过条件神经过程元学习训练将纹理和几何特征编码到隐向量空间，用于在新图像中预测对象的六自由度姿态，实验证明该算法在不同形状和外观的未见对象上具有强大的泛化能力。