  Point cloud is a fundamental 3D representation which is widely used in real
world applications such as autonomous driving. As a newly-developed media
format which is characterized by complexity and irregularity, point cloud
creates a need for compression algorithms which are more flexible than existing
codecs. Recently, autoencoders(AEs) have shown their effectiveness in many
visual analysis tasks as well as image compression, which inspires us to employ
it in point cloud compression. In this paper, we propose a general
autoencoder-based architecture for lossy geometry point cloud compression. To
the best of our knowledge, it is the first autoencoder-based geometry
compression codec that directly takes point clouds as input rather than voxel
grids or collections of images. Compared with handcrafted codecs, this approach
adapts much more quickly to previously unseen media contents and media formats,
meanwhile achieving competitive performance. Our architecture consists of a
pointnet-based encoder, a uniform quantizer, an entropy estimation block and a
nonlinear synthesis transformation module. In lossy geometry compression of
point cloud, results show that the proposed method outperforms the test model
for categories 1 and 3 (TMC13) published by MPEG-3DG group on the 125th
meeting, and on average a 73.15\% BD-rate gain is achieved.
