3D POINT CLOUD CLASSIFICATION WITH GANs: ACGAN and VACWGAN-GP

Download

3D_Point_Cloud_Classification_with_GANs__ACGAN_and_VACWGAN_GP.pdf

Date

2022-2-11

Author

ERGÜN, ONUR

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

249
views

333
downloads

With the developing technology and the power of sensors, 3D data has started to be used in almost every field. Point clouds detected with LIDAR sensors or obtained by sampling 3D meshes have begun to come to the fore in many areas from autonomous driving to data visualization, from generating new data and mesh to classifying detected 3D objects. Machine learning and deep learning techniques are widely used to make sense of this produced data and to implement various applications. In this work, we propose networks to predict the class to which the 3D point cloud belongs, with Auxiliary Classifier Generative Adversarial Network and Versatile Auxiliary Conditional Wasserstein Generative Adversarial Network with Gradient Penalty, which are kind of GANs working with class labeled data. Unlike other classifiers; we are able to enlarge the limited data set with the data produced by taking advantage of the power of generative models, thus we aim to increase the success of the model by training it with more data. As suggested by the ACGAN models, the Discriminator is trained with synthetic data generated by the Generator with using the class label, in addition to the real dataset, ensures that data can be classified while separating real and fake data. Thus, as the training evolves, the Generator is trained to produce more realistic data; which forces Discriminator to classify better. Wasserstein GAN with GP demonstrates similar abilities with a better training by replacing its Discriminator with Critic and modifying its loss function. In this work, we focus on merging Wassterstein GAN-GP with conditional GAN in order to improve the classifier's performance. With this study, the proposed models were tested on 3D datasets and the results were compared with other studies.

Subject Keywords

Machine Learning, Deep Learning, Generative Adverserial Networks(GAN), CGAN, ACGAN, Wasserstein GAN, Wasserstein GAN-GP, Classification, Shape Retrieval

URI

https://hdl.handle.net/11511/96358

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Fuzzy Decision Fusion for Single Target Classification in Wireless Sensor Networks Gok, Sercan; Yazıcı, Adnan; Coşar, Ahmet; George, Roy (2010-07-23) With the advances in technology, low cost and low footprint sensors are being used more and more commonly. Especially for military applications wireless sensor networks (WSN) have become an attractive solution as they have great use for avoiding deadly danger in combat. For military applications, classification of a target in a battlefield plays an important role. A wireless sensor node has the ability to sense the raw signal data in battlefield, extract the feature vectors from sensed signal and produce a ...
Fine‐grained recognition of maritime vessels and land vehicles by deep feature embedding Solmaz, Berkan; Gundogdu, Erhan; Yucesoy, Veysel; Koc, Aykut; Alatan, Abdullah Aydın (2018-12-01) Recent advances in large-scale image and video analysis have empowered the potential capabilities of visual surveillance systems. In particular, deep learning-based approaches bring in substantial benefits in solving certain computer vision problems such as fine-grained object recognition. Here, the authors mainly concentrate on classification and identification of maritime vessels and land vehicles, which are the key constituents of visual surveillance systems. Employing publicly available data sets for ma...
Explainable Security in SDN-Based IoT Networks Sarica, Alper Kaan; Angın, Pelin (2020-12-01) The significant advances in wireless networks in the past decade have made a variety of Internet of Things (IoT) use cases possible, greatly facilitating many operations in our daily lives. IoT is only expected to grow with 5G and beyond networks, which will primarily rely on software-defined networking (SDN) and network functions virtualization for achieving the promised quality of service. The prevalence of IoT and the large attack surface that it has created calls for SDN-based intelligent security solut...
Multi-task Deep Neural Networks in Protein Function Prediction Rifaioğlu, Ahmet Süreyya; Doğan, Tunca; Martin, Maria Jesus; Atalay, Rengül; Atalay, Mehmet Volkan (2017-05-01) In recent years, deep learning algorithms have outperformed the state-of-the art methods in several areas thanks to the efficient methods for training and for preventing overfitting, advancement in computer hardware, the availability of vast amount data. The high performance of multi-task deep neural networks in drug discovery has attracted the attention to deep learning algorithms in bioinformatics area. Here, we proposed a hierarchical multi-task deep neural network architecture based on Gene Ontology (GO...
UNCERTAINTY MODELING FOR EFFICIENT VISUAL ODOMETRY VIA INERTIAL SENSORS ON MOBILE DEVICES AKSOY, Yagiz; Alatan, Abdullah Aydın (2014-10-30) Most of the mobile applications require efficient and precise computation of the device pose, and almost every mobile device has inertial sensors already equipped together with a camera. This fact makes sensor fusion quite attractive for increasing efficiency during pose tracking. However, the state-of-the-art fusion algorithms have a major shortcoming: lack of well-defined uncertainty introduced to the system during the prediction stage of the fusion filters. Such a drawback results in determining covarian...

Citation Formats

O. ERGÜN, “3D POINT CLOUD CLASSIFICATION WITH GANs: ACGAN and VACWGAN-GP,” M.S. - Master of Science, Middle East Technical University, 2022.