  Object Pose Estimation is a crucial component in robotic grasping and
augmented reality. Learning based approaches typically require training data
from a highly accurate CAD model or labeled training data acquired using a
complex setup. We address this by learning to estimate pose from weakly labeled
data without a known CAD model. We propose to use a NeRF to learn object shape
implicitly which is later used to learn view-invariant features in conjunction
with CNN using a contrastive loss. While NeRF helps in learning features that
are view-consistent, CNN ensures that the learned features respect symmetry.
During inference, CNN is used to predict view-invariant features which can be
used to establish correspondences with the implicit 3d model in NeRF. The
correspondences are then used to estimate the pose in the reference frame of
NeRF. Our approach can also handle symmetric objects unlike other approaches
using a similar training setup. Specifically, we learn viewpoint invariant,
discriminative features using NeRF which are later used for pose estimation. We
evaluated our approach on LM, LM-Occlusion, and T-Less dataset and achieved
benchmark accuracy despite using weakly labeled data.
