3D Understanding extends the ability of computer vision systems to understand an image from the image plane to the 3D world. It has become more prominent with off-the-shelf 3D sensors, which have been recently used to build large-scale RGB-D datasets. 3D Understanding addresses problems related to 3D reconstruction, scene understanding (ex: layout prediction), object detection, pose estimation, and others. Applications include but are not limited to automatic inspection, robot navigation, human-machine interaction, and object modeling.
- Bernard Ghanem, Ali Thabet, Juan Carlos Niebles, and Fabian Caba, “Robust Manhattan Frame Estimation from a Single RGB-D Image”, Conference on Computer Vision and Pattern Recognition (CVPR 2015)
- Ali Thabet, Jean Lahoud, Daniel Asmar, Bernard Ghanem, "3D Aware Correction and Completion of Depth Maps in Piecewise Planar Scenes", Asian Conference on Computer Vision (ACCV 2014)
- Bernard Ghanem, Jianming Liang, Jinbo Bi, Marcos Salganicoff, and Arun Krishnan, "Reduction of Lymph Tissue False Positives in Pulmonary Embolism Detection", SPIE Medical Imaging Conference 2007
- M. Bernardine Dias, Bernard Ghanem, and Anthony Stentz, "Improving Cost Estimation in Market-Based Coordination of a Distributed Sensing Task", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005)
- Bernard Ghanem, Ali Fawaz, and Ghassan Karame, "Real-Time Vision-Based Mobile Robot Navigation in Outdoor Environments", 4th annual AUB Student Conference 2005