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.

从弱标记数据中学习姿态估计的方法，通过使用 NeRF 隐式学习物体形状，结合 CNN 使用对比损失学习视图不变特征，从而估计姿态。

利用特征渲染进行 6D 物体姿态估计的 NeRF-Feat

NeRF-Feat: 6D Object Pose Estimation using Feature Rendering

Person re-identification is an important task in video surveillance that aims
to associate people across camera views at different locations and time. View
variability is always a challenging problem seriously degrading person
re-identification performance. Most of the existing methods either focus on how
to learn view invariant feature or how to combine view-wise features. In this
paper, we mainly focus on how to learn view-invariant features by getting rid
of view specific information through a view confusion learning mechanism.
Specifically, we propose an end-toend trainable framework, called View
Confusion Feature Learning (VCFL), for person Re-ID across cameras. To the best
of our knowledge, VCFL is originally proposed to learn view-invariant
identity-wise features, and it is a kind of combination of view-generic and
view-specific methods. Classifiers and feature centers are utilized to achieve
view confusion. Furthermore, we extract sift-guided features by using
bag-of-words model to help supervise the training of deep networks and enhance
the view invariance of features. In experiments, our approach is validated on
three benchmark datasets including CUHK01, CUHK03, and MARKET1501, which show
the superiority of the proposed method over several state-of-the-art approaches

本文提出了一种名为 VCFL 的最新的 Person Re-ID across cameras 的可训练框架，该框架通过利用视图混淆学习机制来学习视图不变的特征，并结合了视图通用性和视图特定性的方法，并利用诸如分类器和特征中心等技术来实现视图混淆。