Thema5126
Abschlussarbeitstyp: Bachelor, Master
Betreuer: Zhan Qu
Forschungsgruppe: Web Science
Archivierungsnummer: 5126
Abschlussarbeitsstatus: Offen
Beginn:
16. Januar 2024
Abgabe: unbekannt
The utilization of Point Cloud data has emerged as a pivotal component in the field of 3D object representation, finding relevance in various domains such as autonomous driving, robotics, drones, and augmented/virtual reality. Within the domain of 3D Point Cloud analysis, fundamental tasks include classification and segmentation. Numerous models have been developed to address these challenges, with Graph-based methods, such as Dynamic Graph Convolutional Neural Network (DGCNN) [1] and its variations, have achieved state-of-the-art performance. However, the lack of explainability limits their practical applicability [2].
The principal aim of this thesis is to develop a novel method to enhance the explainability of Graph-based models for 3D Point Cloud Analysis. By undertaking this thesis, you will not only contribute to bridging the gap between cutting-edge 3D Point Cloud analysis techniques and Graph-based models, but also develop a deep understanding of how to use explainable AI to improve the trustworthiness and interpretability of Deep Neural Networks in real-world applications.
[1] DGCNN
[2] Point Cloud Saliency Maps
Ausschreibung: Download (pdf)