Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Geometric Capsule Autoencoders for 3D Point Clouds   We propose a method to learn object representations from 3D point clouds
using bundles of geometrically interpretable hidden units, which we call
geometric capsules. Each geometric capsule represents a visual entity, such as
an object or a part, and consists of two components: a pose and a feature. The
pose encodes where the entity is, while the feature encodes what it is. We use
these capsules to construct a Geometric Capsule Autoencoder that learns to
group 3D points into parts (small local surfaces), and these parts into the
whole object, in an unsupervised manner. Our novel Multi-View Agreement voting
mechanism is used to discover an object's canonical pose and its pose-invariant
feature vector. Using the ShapeNet and ModelNet40 datasets, we analyze the
properties of the learned representations and show the benefits of having
multiple votes agree. We perform alignment and retrieval of arbitrarily rotated
objects -- tasks that evaluate our model's object identification and canonical
pose recovery capabilities -- and obtained insightful results.

20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg