We present a novel approach to learning a point-wise, meaningful embedding
for point-clouds in an unsupervised manner, through the use of neural-networks.
The domain of point-cloud processing via neural-networks is rapidly evolving,
with novel architectures and applications frequently emerging. Within this
field of research, the availability and plethora of unlabeled point-clouds as
well as their possible applications make finding ways of characterizing this
type of data appealing. Though significant advancement was achieved in the
realm of unsupervised learning, its adaptation to the point-cloud
representation is not trivial. Previous research focuses on the embedding of
entire point-clouds representing an object in a meaningful manner. We present a
deep learning framework to learn point-wise description from a set of shapes
without supervision. Our approach leverages self-supervision to define a
relevant loss function to learn rich per-point features. We train a
neural-network with objectives based on context derived directly from the raw
data, with no added annotation. We use local structures of point-clouds to
incorporate geometric information into each point's latent representation. In
addition to using local geometric information, we encourage adjacent points to
have similar representations and vice-versa, creating a smoother, more
descriptive representation. We demonstrate the ability of our method to capture
meaningful point-wise features through three applications. By clustering the
learned embedding space, we perform unsupervised part-segmentation on point
clouds. By calculating euclidean distance in the latent space we derive
semantic point-analogies. Finally, by retrieving nearest-neighbors in our
learned latent space we present meaningful point-correspondence within and
among point-clouds.

本文提出了一种无监督学习点云的有意义的点对点嵌入方法，利用神经网络的自我监督来训练，从而实现对无标签数据进行语义分割。

PointWise: 一种无监督的点级特征学习网络

PointWise: An Unsupervised Point-wise Feature Learning Network

Machine-learning tasks frequently involve problems of manipulating and
classifying large numbers of vectors in high-dimensional spaces. Classical
algorithms for solving such problems typically take time polynomial in the
number of vectors and the dimension of the space. Quantum computers are good at
manipulating high-dimensional vectors in large tensor product spaces. This
paper provides supervised and unsupervised quantum machine learning algorithms
for cluster assignment and cluster finding. Quantum machine learning can take
time logarithmic in both the number of vectors and their dimension, an
exponential speed-up over classical algorithms.

本文提出了适用于聚类分配和聚类查找的有监督和无监督的量子机器学习算法，利用量子计算能够处理高维向量的优势，实现了对于大量向量的分类问题的指数级加速。