Abstract:
Point Clouds are an imminent source of the scalable geometric data structure.
They are widely used in computer graphics or computer graphic applications.
Point clouds are in unstructured form, due to irregularities in structure the
shapes are not organized in a pre-defined manner. In contrast to deep learning methods, voxelization is computationally expensive due to unnecessary
voluminous data. An efficient model is required whose performance is not
obstructed due to middleware dependency like in case of voxels, it is difficult
to capture high-resolution features which neglect the geometric relationship
between its neighbors. With the advent of deep learning and computer vision
got a boost in solving problems efficiently using convolution neural networks
from object recognition, and part segmentation. It is an extremely challenging task to deal with unstructured 3D point clouds using a deep neural
network. To overcome this issue, we designed a new deep net architecture
that directly takes raw point clouds as input. This paper proposed a novel
deep architecture dubbed with edge convolution and feature transformation
layers suitable for performing high-level point cloud processing tasks, including 3D object classification and part segmentation. The model works as
each point of a point cloud is processed independently while maintaining
geometric properties among its neighborhoods. The whole process is a recursive task where each point of the point cloud is used as an input to learn
more fine-grained geometric features along with their semantic properties
from potentially long distances. This novel approach achieves 1.0% better
performance on 3D object recognition and part segmentation using standard
datasets including ModelNet40 for object recognition and Shape Net part for
part segmentation.
