Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data.
In this paper, we first show that a straightforward extension of an existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data.
We propose a two fold modification to a GAN algorithm to be able to generate point clouds (PC-GAN).
First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process.
A key component of our method is that we train a posterior inference network for the hidden variables.
Second, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms.
We further propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form in WGAN.
We validate our claims on the ModelNet40 benchmark dataset and observe that PC- GAN trained by the sandwiching objective achieves better results on test data than existing methods.
We also conduct studies on several tasks, including generalization on unseen point clouds, latent space interpolation, classification, and image to point clouds transformation, to demonstrate the versatility of the proposed PC-GAN algorithm.
A fundamental problem in machine learning is that given a data set, learn a generative model that can efficiently generate arbitrary many new sample points from the domain of the underlying distribution BID5 .
Deep generative models use deep neural networks as a tool for learning complex data distributions BID31 BID43 BID18 .
Especially, Generative Adversarial Networks (GAN) BID18 has drawn attention because of its success in many applications.
Compelling results have been demonstrated on different types of data, including text, images, and videos BID28 Vondrick et al., 2016) .
Their wide range of applicability was also shown in many important problems, including data augmentation (Salimans et al., 2016) , image style transformation BID24 , image captioning BID10 , and art creations BID27 .Recently
, capturing 3D information is garnering attention. There are
many different data types for 3D information, such as CAD, 3D meshes, and point clouds. 3D point
clouds are getting popular since these store more information than 2D images and sensors capable of collecting point clouds have become more accessible. These include
Lidar on self-driving cars, Kinect for Xbox, and face identification sensor on phones. Compared to other
formats, point clouds can be easily represented as a set of points, which has several advantages, such as permutation invariance of the set members. The algorithms which
can effectively learn from this type of data is an emerging field (Qi et al., 2017a; Zaheer et al., 2017; BID26 BID15 . However, compared to
supervised learning, unsupervised generative models for 3D data are still under explored BID0 BID42 .Extending existing GAN
frameworks to point clouds or more generally set data is not straightforward. In this paper, we begin
by formally defining the problem and discussing its difficulty (Section 2). Circumventing the challenges
, we propose a deep generative adversarial network (PC-GAN) with a hierarchical sampling and inference network for point clouds. The proposed architecture learns
a stochastic procedure which can generate new point clouds and draw samples from the generated point clouds without explicitly modeling the underlying density function (Section 3). The proposed PC-GAN is a generic
algorithm which can incorporate many existing GAN variants. By utilizing the property of point
clouds, we further propose a sandwiching objective by considering both upper and lower bounds of Wasserstein distance estimate, which can lead to tighter approximation (Section 3.1). Evaluation on ModelNet40 shows excellent
generalization capability of PC-GAN. We first demonstrate that we can sample
from the learned model to generate new point clouds and the latent representations learned by the inference network provide meaningful interpolations between point clouds. Then we show the conditional generation
results on unseen classes of objects, which demonstrates the superior generalization ability of PC-GAN. Lastly, we also provide several interesting
studies, such as classification and point clouds generation from images (Section 5).
In this paper, we first showed a straightforward extension of existing GAN algorithm is not applicable to point clouds.
We then proposed a GAN modification (PC-GAN) that is capable of learning to generate point clouds by using ideas both from hierarchical Bayesian modeling and implicit generative models.
We further propose a sandwiching objective which results in a tighter Wasserstein distance estimate theoretically and better performance empirically.In contrast to some existing methods BID0 , PC-GAN can generate arbitrary as many i.i.d. points as we need to form a point clouds without pre-specification.
Quantitatively, PC-GAN achieves competitive or better results using smaller network than existing methods.
We also demonstrated that PC-GAN can capture delicate details of point clouds and generalize well even on unseen data.
Our method learns "point-wise" transformations which encourage the model to learn the building components of the objects, instead of just naively copying the whole object.
We also demonstrate other interesting results, including point cloud interpolation and image to point clouds.Although we only focused on 3D applications in this paper, our framework can be naturally generalized to higher dimensions.
In the future we would like to explore higher dimensional applications, where each 3D point can have other attributes, such as RGB colors and 3D velocity vectors.
We propose a GAN variant which learns to generate point clouds. Different studies have been explores, including tighter Wasserstein distance estimate,  conditional generation, generalization to unseen point clouds and image to point cloud.