Geometry Identification from Point Cloud Data
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2021
Författare
Deepak, Maitreya Dave
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Abstract
Abstract: The aim of this thesis is to conduct a comparative study of different algorithms
which can learn from 3D (x,y,z) point cloud data and are able to conduct
per-point classification. The point clouds of interest for this thesis are the one’s
which represent a 360-degree view of an object. For example, the input is a point
cloud representing a knife with two parts: the blade and the handle. For such an
input, the algorithms must be able to classify which points of this point cloud belong
to blade and to the handle. To solve this task different types of deep-learning based
models like graph-based neural networks and non-grid point convolution networks
were implemented and analyzed. These models perform with over 80% accuracy
which is quite remarkable given the input is only raw 3D coordinates with no need
of voxelization. The models have also been tested on synthetic datasets as well as
an object scanned using a LiDAR camera. These algorithms can be applied in autonomous
driving, augmented/virtual reality applications, medical data processing
and 3D animation industry.
Keywords:
Beskrivning
Ämne/nyckelord
point, cloud, part, segmentation, pointnet, dgcnn, kpconv, 3D, deeplearning, supervised