Shape Based Classification and Segmentation Of 3D Point Clouds using Deep Learning

DOI

10.2991/978-94-6463-314-6_6How to use a DOI?

Keywords

3D point cloud; Classification; Segmentation; Deep Learning

Abstract

The recognition of 3D objects that are represented as point cloud has become a very vital topic for research in the field of Computer Science. The processing of 3D point clouds has procured a lot of attention from researchers in the modern era. However, as point clouds have a complex representation, it is still a challenging task to capture 3D objects using LiDAR (Light Detection and Ranging) devices. The goal of our work is to identify an object that is represented as a point cloud accurately using 3D Deep Learning. The scope of the project is very vast, and its applications include AR/VR (augmented/virtual reality), self- driving vehicles, robotics, etc. The work aims to classify the 3D objects and also to identify various integrated parts of the object by performing segmentation by accessing the 3d point clouds directly on modelnet10 and shapenet for classification and segmentation respectively. In classification, the captured object is classified into several classes such as a chair, table, nightstand, sofa, bed, etc. Using the most suitable deep learning model we perform classification on the point clouds obtained from the ModelNet10 dataset and segment the objects into parts using shapenet dataset. Also, experimentation was done on different learning rate parameters for better results.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - D. Jyothsna
AU  - G. Ramesh Chandra
PY  - 2023
DA  - 2023/12/21
TI  - Shape Based Classification and Segmentation Of 3D Point Clouds using Deep Learning
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 53
EP  - 61
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_6
DO  - 10.2991/978-94-6463-314-6_6
ID  - Jyothsna2023
ER  -