About Jean Lahoud Jean Lahoud Ph.D., Electrical and Computer Engineering Computer Vision 3D object detection Deep learning Jean Lahoud was a Ph. D. student in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Jean Lahoud received his Bachelor of Engineering in Mechanical Engineering from Notre Dame University in Lebanon in 2012. In 2014, he obtained his Master of Engineering degree in the same field from American University of Beirut in Lebanon. Before joining KAUST, Jean was a product manager at Murex systems in Lebanon. Research Interest Jean Lahoud is interested in 3D scene understanding using RGB-D sensors. Honors and Awards In Events Presented Events May 24 - May 30, 2020 Indoor 3D Scene Understanding Using Depth Sensors Jean Lahoud, Ph.D., Electrical and Computer Engineering May 28, 16:00 - 18:00 KAUST Computer Vision 3D object detection Deep learning One of the main goals in computer vision is to achieve a human-like understanding of images. This understanding has been recently represented in various forms, including image classification, object detection, semantic segmentation, among many others. Nevertheless, image understanding has been mainly studied in the 2D image frame, so more information is needed to relate them to the 3D world. With the emergence of 3D sensors (e.g. the Microsoft Kinect), which provide depth along with color information, the task of propagating 2D knowledge into 3D becomes more attainable and enables interaction between a machine (e.g. robot) and its environment. This dissertation focuses on three aspects of indoor 3D scene understanding: (1) 2D-driven 3D object detection for single frame scenes with inherent 2D information, (2) 3D object instance segmentation for 3D reconstructed scenes, and (3) using room and floor orientation for automatic labeling of indoor scenes that could be used for self-supervised object segmentation. These methods allow capturing of physical extents of 3D objects, such as their sizes and actual locations within a scene.
Indoor 3D Scene Understanding Using Depth Sensors Jean Lahoud, Ph.D., Electrical and Computer Engineering May 28, 16:00 - 18:00 KAUST Computer Vision 3D object detection Deep learning One of the main goals in computer vision is to achieve a human-like understanding of images. This understanding has been recently represented in various forms, including image classification, object detection, semantic segmentation, among many others. Nevertheless, image understanding has been mainly studied in the 2D image frame, so more information is needed to relate them to the 3D world. With the emergence of 3D sensors (e.g. the Microsoft Kinect), which provide depth along with color information, the task of propagating 2D knowledge into 3D becomes more attainable and enables interaction between a machine (e.g. robot) and its environment. This dissertation focuses on three aspects of indoor 3D scene understanding: (1) 2D-driven 3D object detection for single frame scenes with inherent 2D information, (2) 3D object instance segmentation for 3D reconstructed scenes, and (3) using room and floor orientation for automatic labeling of indoor scenes that could be used for self-supervised object segmentation. These methods allow capturing of physical extents of 3D objects, such as their sizes and actual locations within a scene.
Related Sites Electrical and Computer Engineering (ECE) Image and Video Understanding Lab (IVUL) Related Content Events 1 Related Links Also view list of Publications on KAUST Repository Image and Video Understanding Lab (IVUL)
