| KEYWORD |
Microelectronics
Real-Time 3D Object Pose Estimator using AI on Edge
Tesi esterna in azienda
Parole chiave IMAGE ANALYSIS, MACHINE LEARNING
Riferimenti LUCIANO LAVAGNO
Riferimenti esterni Marcello Babbi, Reply Torino
Gruppi di ricerca Microelectronics
Tipo tesi APPLIED RESEARCH
Descrizione The accurate and real-time estimation of 3D poses of objects has many practical applications in various
domains, such as robotics, smart agriculture and augmented reality. This thesis proposes the development of
a state-of-the-art 3D pose estimator using AI and deploying it on edge devices. The system will use computer
vision and deep learning techniques to analyze 3D point clouds and estimate the pose of specified objects,
enabling accurate and efficient object tracking and recognition. The thesis will focus on implementing the
latest deep neural network models for 3D pose estimation, data processing, and feature engineering. The
system will be designed to run on edge devices, enabling real-time processing of images and immediate
feedback to the user.
Conoscenze richieste Python programming, some knowledge of Machine Learning, image processing
Note The student will be involved in:
❑ State-of-the-art literature review
❑ SW requirements definition for edge deployment
❑ Data pre-processing and feature engineering
❑ Developing and implementing a deep learning-based model for 3D point cloud analysis
❑ HW computational requirements trade-off
Scadenza validita proposta 11/10/2024
PROPONI LA TUA CANDIDATURA