{"id":23,"date":"2019-08-16T15:04:11","date_gmt":"2019-08-16T15:04:11","guid":{"rendered":"http:\/\/cs.smu.ca\/~jiju\/?page_id=23"},"modified":"2026-03-26T11:11:46","modified_gmt":"2026-03-26T14:11:46","slug":"dr-jiju-poovvancheri","status":"publish","type":"page","link":"https:\/\/cs.smu.ca\/~jiju\/","title":{"rendered":"Dr. Jiju Poovvancheri"},"content":{"rendered":"\n
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An Associate Professor in the Department of Math & Computing Science at Saint Mary\u2019s University, Halifax, Canada, with prior postdoctoral appointments at the Graphics Lab, Department of Computer Science, University of Victoria, and the Geospatial Intelligence Lab, University of Calgary. Completed a PhD at the Indian Institute of Technology Madras (IIT Madras) under the supervision of Prof. Ramanathan Muthuganapathy<\/strong><\/a>, and a Master’s degree at the National Institute of Technology Karnataka.<\/p>\n<\/div><\/div>\n\n\n\n

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Research Interests<\/strong><\/h2>\n\n\n\n

My research lies at the intersection of 3D computer vision, computer graphics, geometric learning, and spatial computing<\/strong>, with a focus on developing scalable, learning-based algorithms for understanding and modeling large-scale 3D environments. Motivated by challenges in urban, environmental and planetary sensing<\/em>, my work emphasizes principled integration of geometry, topology, and machine learning<\/em> for robust perception from 3D data.<\/p>\n\n\n\n

A central theme of my research is the analysis of point clouds acquired from LiDAR and RGB-D sensors<\/strong>, where I design methods that are both computationally efficient and structurally aware. Rather than treating 3D data as unstructured inputs, my work leverages spatial data structures, geometric priors, and topological representations to improve scalability, generalization, and interpretability of learning-based models.<\/p>\n\n\n\n

My recent and ongoing work focuses on:<\/p>\n\n\n\n