This project aims to provide an implementation of Point Cloud Compression (PCC) methods for segmented point clouds.

Developed together with Saverio Cavasin (@SvrCvs)

The dataset is the SELMA dataset, which is made by data collected by of 3 LIDARs located on a vehicle in a urban scenario. The location of the sensors is the following:

An example of the dataset is shown in the figure below:

The point clouds are segmented in different clsses, widely discussed here.

is a compression library for 3D geometric meshes and point clouds. It is based on the Google Draco library, which is a general-purpose 3D geometry compression library.

is a density-based clustering algorithm. It is a popular algorithm for clustering in a spatial context. The algorithm groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away).

trained to learn a representation (encoding) of a sample, with the lower possible loss in the reconstruction of the input data. The network is composed by an encoder and a decoder. The encoder compresses the input data into a lower dimensional space, while the decoder reconstructs the input data from the compressed representation. The architecture of the network is the following:

The training is done on 400 Point clouds of each class, and the test is done on 100 point clouds of each class. The results are the following:

Some examples of the reconstruction are the following:

Finally, we propose to change the coordinate system of the point cloud from cartesian to spherical and then project the point cloud on a 2D plane, whith a specific grid size. Some examples are the following: