LEE- Bilgisayar Mühendisliği Lisansüstü Programı

Bu topluluk için Kalıcı Uri

http://hdl.handle.net/11527/19206

Gözat

Konu "3D point cloud" ile LEE- Bilgisayar Mühendisliği Lisansüstü Programı'a göz atma
  • Öge
    Deep learning-based techniques for 3D point cloud analysis
    (Graduate School, 2023-10-10) Şahin, Yusuf Hüseyin ; Ünal, Gözde ; 504172510 ; Computer Engineering
    This thesis presents two innovative works in the field of point cloud processing: ODFNet and ALReg. The ODFNet work proposes a new method for the classification and segmentation of point clouds, while ALReg aims to facilitate the training of neural networks for point cloud registration using active learning. Convolutional neural networks (CNNs) have been widely used for visual tasks such as object categorization, object detection, and semantic segmentation. However, the application of CNNs to point clouds is a relatively recent development. Notably, there has been no previous approach that specifically utilizes point density information. To enhance the representational power of local features, we propose leveraging the distribution of point orientations within a neighborhood relative to a reference point. This led us to introduce the concept of point Orientation Distribution Functions (ODFs). In order to compute Orientation Distribution Functions (ODFs), our approach involves dividing the spherical region around each point into a set of cones aligned with predefined orientations. Within each cone, we calculate the density of points, resulting in the ODFs for that particular point. These ODF patterns provide valuable information about the spatial structure and orientation characteristics of the objects within the point cloud. The ODFs allow us to summarize the local neighborhood structure of the point cloud in a concise manner, which is beneficial for our point cloud analysis network model design. By incorporating the ODFs, we can effectively capture and utilize the significant local geometric information present in the point cloud, leading to improved performance and accuracy in our analysis tasks. Additionally, we introduce the ODFNet, a neural network model specifically designed for point cloud analysis tasks. The ODFNet utilizes the ODFBlock and ODFs to effectively incorporate the directional information captured by the ODFs. This integration enhances the performance of various tasks, such as classification, segmentation, and other related applications, by leveraging the rich geometric information provided by the ODFs. The experimental results confirm that ODFNet achieves state-of-the-art performance in both classification and segmentation tasks. Point cloud registration, which involves aligning multiple point clouds by calculating the rigid transformation between them, is a crucial task in computer vision. While there have been significant advancements in point cloud registration using various methods, most of them rely on training the network with the entire shape for registration. However, an alternative approach that has not been explored in the literature is active learning, which could also be employed to address this problem. Thus, in this thesis, we also introduce ALReg, an active learning approach aimed at improving the efficiency of point cloud registration by selectively utilizing informative regions to reduce training time. To achieve this, we modify the baseline registration networks by incorporating Monte Carlo DropOut (MCDO) to efficiently calculate uncertainty. Our main objective is to demonstrate that similar accuracy scores can be achieved by using fewer point clouds or parts of point clouds in the training phase of any point cloud registration network, provided that the selection of these training samples/parts is done effectively. By leveraging ALReg, our goal is to incorporate the most effective point cloud parts into the training procedure, thereby improving the efficiency and effectiveness of the registration network.
  • Öge
    Towards robustness in 3D point cloud analysis: Novel approaches to adversarial attacks and defences
    (Graduate School, 2025-01-21) Cengiz, Batuhan ; Ünal, Gözde ; 504211550 ; Computer Engineering
    This thesis explores the domain of adversarial robustness in 3D point cloud data, addressing both the offensive and the defensive aspects of adversarial interactions. The subject focuses on designing methods for adversarial attacks and defence mechanisms, particularly for applications in safety-critical domains like autonomous driving, robotics, and facial recognition. The first part of the study introduces a novel adversarial attack method, named the ε-Mesh Attack. This method confines perturbations to the surface of 3D meshes, preserving the structural integrity of facial data. Unlike traditional approaches that operate within a 3D ε-ball, the ε-Mesh Attack reduces the optimization domain to 2D triangular planes by employing two projection methods: Central projection and Perpendicular projection. These methods ensure that adversarial manipulations remain realistic while misleading classification models. Evaluations were conducted using PointNet and DGCNN models trained on well-known 3D datasets. The results demonstrate that the ε-Mesh Attack effectively compromises model performance while maintaining the original surface integrity. In the second part, the thesis proposes a novel defence mechanism called Point Cloud Layerwise Diffusion (PCLD). PCLD enhances robustness by employing a diffusion-based purification process that operates layer by layer within the neural network. The method involves training diffusion probabilistic models for each layer of a classifier, enabling hierarchical purification of adversarial perturbations. Suggested Point Cloud Layerwise Diffusion method was tested against state-of-the-art defence techniques and showed superior or comparable performance, particularly in defending against deeper-layer attacks. The conclusions derived from this research emphasize the importance of preserving structural integrity during adversarial attacks and the effectiveness of layerwise purification in defending against such attacks. The findings contribute to advancing secure and resilient 3D point cloud processing methods, paving the way for their safe deployment in critical applications. Future work aims to extend these methods into the temporal domain and adapt them to handle emerging adversarial strategies effectively.