  Motivated by the need for estimating the 3D pose of arbitrary objects, we
consider the challenging problem of class-agnostic object viewpoint estimation
from images only, without CAD model knowledge. The idea is to leverage features
learned on seen classes to estimate the pose for classes that are unseen, yet
that share similar geometries and canonical frames with seen classes. We train
a direct pose estimator in a class-agnostic way by sharing weights across all
object classes, and we introduce a contrastive learning method that has three
main ingredients: (i) the use of pre-trained, self-supervised, contrast-based
features; (ii) pose-aware data augmentations; (iii) a pose-aware contrastive
loss. We experimented on Pascal3D+, ObjectNet3D and Pix3D in a cross-dataset
fashion, with both seen and unseen classes. We report state-of-the-art results,
including against methods that additionally use CAD models as input.
