Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Detection of Distinctive Regions on 3D Shapes   This paper presents a novel approach to learn and detect distinctive regions
on 3D shapes. Unlike previous works, which require labeled data, our method is
unsupervised. We conduct the analysis on point sets sampled from 3D shapes,
then formulate and train a deep neural network for an unsupervised shape
clustering task to learn local and global features for distinguishing shapes
with respect to a given shape set. To drive the network to learn in an
unsupervised manner, we design a clustering-based nonparametric softmax
classifier with an iterative re-clustering of shapes, and an adapted
contrastive loss for enhancing the feature embedding quality and stabilizing
the learning process. By then, we encourage the network to learn the point
distinctiveness on the input shapes. We extensively evaluate various aspects of
our approach and present its applications for distinctiveness-guided shape
retrieval, sampling, and view selection in 3D scenes.

28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph