  With the immense growth of dataset sizes and computing resources in recent
years, so-called foundation models have become popular in NLP and vision tasks.
In this work, we propose to explore foundation models for the task of keypoint
detection on 3D shapes. A unique characteristic of keypoint detection is that
it requires semantic and geometric awareness while demanding high localization
accuracy. To address this problem, we propose, first, to back-project features
from large pre-trained 2D vision models onto 3D shapes and employ them for this
task. We show that we obtain robust 3D features that contain rich semantic
information and analyze multiple candidate features stemming from different 2D
foundation models. Second, we employ a keypoint candidate optimization module
which aims to match the average observed distribution of keypoints on the shape
and is guided by the back-projected features. The resulting approach achieves a
new state of the art for few-shot keypoint detection on the KeyPointNet
dataset, almost doubling the performance of the previous best methods.
