We tackle the problem of learning the geometry of multiple categories of
deformable objects jointly. Recent work has shown that it is possible to learn
a unified dense pose predictor for several categories of related objects.
However, training such models requires to initialize inter-category
correspondences by hand. This is suboptimal and the resulting models fail to
maintain correct correspondences as individual categories are learned. In this
paper, we show that improved correspondences can be learned automatically as a
natural byproduct of learning category-specific dense pose predictors. To do
this, we express correspondences between different categories and between
images and categories using a unified embedding. Then, we use the latter to
enforce two constraints: symmetric inter-category cycle consistency and a new
asymmetric image-to-category cycle consistency. Without any manual annotations
for the inter-category correspondences, we obtain state-of-the-art alignment
results, outperforming dedicated methods for matching 3D shapes. Moreover, the
new model is also better at the task of dense pose prediction than prior work.

本研究利用统一嵌入来增强不同类别之间和图像与类别之间的自动一致性，从而无需手动注释即可获得最新的对齐结果和精准的密集姿势预测。

通过通用规范映射发现物体类别之间的关系

Discovering Relationships between Object Categories via Universal  Canonical Maps

Automatic discovery of category-specific 3D keypoints from a collection of
objects of some category is a challenging problem. One reason is that not all
objects in a category necessarily have the same semantic parts. The level of
difficulty adds up further when objects are represented by 3D point clouds,
with variations in shape and unknown coordinate frames. We define keypoints to
be category-specific, if they meaningfully represent objects' shape and their
correspondences can be simply established order-wise across all objects. This
paper aims at learning category-specific 3D keypoints, in an unsupervised
manner, using a collection of misaligned 3D point clouds of objects from an
unknown category. In order to do so, we model shapes defined by the keypoints,
within a category, using the symmetric linear basis shapes without assuming the
plane of symmetry to be known. The usage of symmetry prior leads us to learn
stable keypoints suitable for higher misalignments. To the best of our
knowledge, this is the first work on learning such keypoints directly from 3D
point clouds. Using categories from four benchmark datasets, we demonstrate the
quality of our learned keypoints by quantitative and qualitative evaluations.
Our experiments also show that the keypoints discovered by our method are
geometrically and semantically consistent.

本文提出了一种采用非监督学习的方法，从未知类别的物体的三维点云中学习特定于类别的三维关键点，使用对称线性基础形状模型描述特定于类别的形状，并证明了该方法能够得到几何和语义方面的一致的关键点。