3D Representation and Analytics for Computer Vision
Abstract:
Data in computer vision is commonly either in the homogeneous format in 2D projective space (e.g., images and videos) or a heterogeneous format in 3D Euclidean space (e.g., point clouds). One advantage of 3D data is its invariancy towards illumination-based appearance. 3D point clouds are now a vital data source for vision tasks such as autonomous driving, robotic perception, and scene understanding. Thus, deep learning for 3D points has become an essential branch of geometry-based research. However, deep learning over unstructured point clouds is quite challenging. Moreover, due to the 3D data explosion, new representations are necessary for compression and encryption. Therefore, we introduce a new sphere-based representation (3DSaint) to model a 3D scene and further utilize it in deep networks. Our representation produces state-of-the-art results on several 3D understanding tasks, including 3D shape classification. We also present differential geometry-based networks for the 3D analysis tasks.
Bio:
Dr. Chandra Kambhamettu is a Full Professor of the Computer Science department at the University of Delaware, where he directs the Video/Image Modelling and Synthesis (VIMS) group. His research interests span Artificial Intelligence and Data Science, including computer vision, machine learning, and big data visual analytics. His Lab focuses on novel schemes for multimodal image analysis methodologies. Some of his recent research includes image analysis for the visually impaired, 3D point cloud analysis, drone and vehicular-based camera imagery acquisition, analysis and reconstruction, and plant science image analysis. His work on nonrigid structure from motion was published in CVPR in 2000 and cited as one of the first two papers in the field of Nonrigid Structure from Motion. Several of Dr. Kambhamettu’s works also focus on problems that highly impact earth life, such as arctic sea ice observations with application towards mammal habitat quantification and climate change and hurricane image studies, among several others. Before joining UD, he was a research scientist at NASA-Goddard, where he received the “1995 Excellence in Research Award.” In addition, he received the NSF CAREER award in 2000 and NASA’s “Group Achievement Award” in 2013 for his work in the deployment of the Arctic Collaborative Environment.