| الوصف: |
open ; In this thesis I explore the processing of 3D data and its industrial applications, utilizing both traditional computer vision techniques and modern methods based on deep learning. The ability to sense, perceive, and interpret the surrounding environment by a computer is a challenging task that requires a mathematical framework. While most research has historically focused on 2D data, the recent availability of more affordable 3D sensors and the advancement of powerful deep learning tools have made it possible to tackle tasks that were previously out of reach with standard 2D techniques. The thesis is divided into three parts. The first part provides an overview of the theory and methods that form the foundation of the applications developed in the subsequent parts. It begins with techniques and sensors for acquiring 3D data, followed by a discussion on the different ways to represent this information. It then delves into high-level 3D computer vision tasks, covering both traditional approaches as well as modern techniques using deep learning networks. The second part presents a deep learning application that I developed to address a 3D classification task. The network architecture is inspired by the Orientation Boosted Voxel Net, where the network is trained to learn object rotations as an auxiliary task using a combined categorical cross-entropy loss function. The novelty of my design lies in the complete redefinition of the architecture, where I employed skip connections to enable a deeper network, thereby avoiding vanishing gradient problems and facilitating more abstract and effective feature extraction. The full implementation of the dataset, model, network training, and testing was carried out in Python. The third part of the thesis demonstrates the application of the methods discussed for the design of an industrial system that I developed during my internship at Innova Srl. The aim was to create a general module capable of acquiring point clouds of objects moving on an industrial conveyor belt, ... |