  Point clouds, being the simple and compact representation of surface geometry
of 3D objects, have gained increasing popularity with the evolution of deep
learning networks for classification and segmentation tasks. Unlike human,
teaching the machine to analyze the segments of an object is a challenging task
and quite essential in various machine vision applications. In this paper, we
address the problem of segmentation and labelling of the 3D point clouds by
proposing a inception based deep network architecture called PIG-Net, that
effectively characterizes the local and global geometric details of the point
clouds. In PIG-Net, the local features are extracted from the transformed input
points using the proposed inception layers and then aligned by feature
transform. These local features are aggregated using the global average pooling
layer to obtain the global features. Finally, feed the concatenated local and
global features to the convolution layers for segmenting the 3D point clouds.
We perform an exhaustive experimental analysis of the PIG-Net architecture on
two state-of-the-art datasets, namely, ShapeNet [1] and PartNet [2]. We
evaluate the effectiveness of our network by performing ablation study.
