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Prof Niloy Mitra
6.17 MPEB
Dept. of Computer Science
Gower Street
Tel: +44 (0)20 7679 0322
Fax: +44 (0)20 7387 1397
  • Professor of Geometry Processing
  • Dept of Computer Science
  • Faculty of Engineering Science

Dr. Mitra is a Reader in Geometric Modeling and Computer Graphics in the Department of Computer Science, University College London (UCL). He was a Senior Lecturer at UCL from 2011-2012. Earlier, Dr. Mitra cofounded the Geometric Modeling and Scientific Visualization (GMSV) center at KAUST (2009-2011) and was an Assistant Professor at Indian Institute of Technology (IIT) Delhi (2007-2009). In 2006-2007, Dr. Mitra was a postdoctoral scholar with Prof. Helmut Pottmann at Technical University Vienna. He received his MS (2002) and PhD (Aug. 2006) in Electrical Engineering from Stanford University under the guidance of Prof. Leonidas Guibas and Prof. Marc Levoy (associate advisor). He received his BS (advisor Prof. Prabir Biswas) from Indian Institute of Technology (IIT) Kharagpur.

Research Groups
Research Themes
Research Summary
3D geometry (e.g., point clouds, triangle soups, meshes) is now easy to acquire (e.g., using Microsoft Kinect), model (e.g., using Blender), or simply download from model repositories (e.g, Turbosquid). Such low-level data, however, makes subsequent handling and processing difficult, and hinders efficient use of model collections. Hence, I focus on developing computational frameworks to extract high-level geometric abstractions from unorganized and heterogeneous 3D data. Specifically, I concentrate on the following themes:

(i) Shape Understanding: Algorithmically understand important relations between objects and object parts in 3D objects, e.g., identify symmetries, detect regular structures, create shape abstractions, or understand invariants and variabilties across model collections.

(ii) Smart Manipulation: Abstract input geometry into structure and variations to explicitly encode non-local constraints. Beyond immediate application to denoising and smart reconstruction, such abstractions immediately allows intuitive and powerful shape manipulation, image-based editing, and constrained shape space exploration.

(iii) Computational Design: The ability to characterize design spaces using collection of (non-linear) constraints opens the possibility to directly optimize on design manifolds leading to fabrication-aware design, guided shape exploration, and in general computational design.

Teaching Summary

AT UCL, Dr. Mitra teaches Image Processing (GV12/COMP3072) in the fall term and Geometry Processing (GV18/M080) in the winter term. Dr. Mitra has also conducted several international summer schools over the last years and organized invited sessions at EUROGRAPHICS conferences (2010-2013) on advanced topics in geometry processing, shape understanding, and image processing. 

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