  Over the last decade, the demand for better segmentation and classification
algorithms in 3D spaces has significantly grown due to the popularity of new 3D
sensor technologies and advancements in the field of robotics. Point-clouds are
one of the most popular representations to store a digital description of 3D
shapes. However, point-clouds are stored in irregular and unordered structures,
which limits the direct use of segmentation algorithms such as Convolutional
Neural Networks. The objective of our work is twofold: First, we aim to provide
a full analysis of the PointNet architecture to illustrate which features are
being extracted from the point-clouds. Second, to propose a new network
architecture called IPC-Net to improve the state-of-the-art point cloud
architectures. We show that IPC-Net extracts a larger set of unique features
allowing the model to produce more accurate segmentations compared to the
PointNet architecture. In general, our approach outperforms PointNet on every
family of 3D geometries on which the models were tested. A high generalisation
improvement was observed on every 3D shape, especially on the rockets dataset.
Our experiments demonstrate that our main contribution, inter-point activation
on the network's layers, is essential to accurately segment 3D point-clouds.
