As robotic systems increasingly encounter complex and unconstrained
real-world scenarios, there is a demand to recognize diverse objects. The
state-of-the-art 6D object pose estimation methods rely on object-specific
training and therefore do not generalize to unseen objects. Recent novel object
pose estimation methods are solving this issue using task-specific fine-tuned
CNNs for deep template matching. This adaptation for pose estimation still
requires expensive data rendering and training procedures. MegaPose for example
is trained on a dataset consisting of two million images showing 20,000
different objects to reach such generalization capabilities. To overcome this
shortcoming we introduce ZS6D, for zero-shot novel object 6D pose estimation.
Visual descriptors, extracted using pre-trained Vision Transformers (ViT), are
used for matching rendered templates against query images of objects and for
establishing local correspondences. These local correspondences enable deriving
geometric correspondences and are used for estimating the object's 6D pose with
RANSAC-based PnP. This approach showcases that the image descriptors extracted
by pre-trained ViTs are well-suited to achieve a notable improvement over two
state-of-the-art novel object 6D pose estimation methods, without the need for
task-specific fine-tuning. Experiments are performed on LMO, YCBV, and TLESS.
In comparison to one of the two methods we improve the Average Recall on all
three datasets and compared to the second method we improve on two datasets.

通过使用预训练的 Vision Transformers（ViT）提取的视觉描述符，我们引入了 ZS6D 方法，用于零样本新目标 6D 姿态估计，该方法在众多最新的姿态估计方法上表现卓越，无需进行特定任务的微调。我们在 LMO、YCBV 和 TLESS 等三个数据集上进行了实验，并与两种方法进行了比较，结果在所有三个数据集上相较于其中一种方法有显著提升，在两个数据集上相较于另一种方法有提升。