Research

My research interests lie at the intersection of computer vision and graphics. More specifically, I am interested in
  • Algorithms and techniques for recovering and modeling the appearance, shape and motion of real-world objects from video or multiple views;
  • Novel data-based representations: local and image-based representations for rendering/visualization of real-world objects;
  • Methods for data and example-based learning: extracting priors/patterns and probabilistic models for shape/motion recovery and model acquisition.

My thesis work focuses on probabilistic formulations for recovering 3D shape and motion from images and addresses the issues of building theoretically sound frameworks from first principles, learning strong priors and likelihood distributions from example shapes and images, and the design of probabilistic reconstruction algorithms.


Recent projects



A Non-parametric synthesis-based approach to 3D Reconstruction


slice1 slice2 slice3 Probabilistic 3D Occupancy from noisy photographs


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3D Tracking of non-rigid shapes in video
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   N-view stereo: Probabilistic Inference of 3D Shape from Noisy Photographs

      Abstract:

This work focuses on the inference of 3D shape from a set of N noisy photos. We derive a probabilistic framework to specify what one can infer about 3D shape for arbitrarily-shaped, Lambertian scenes and arbitrary viewpoint configurations. Based on formal definitions of visibility, occupancy, emptiness, and photo-consistency, the theoretical development yields a formulation of the Photo Hull Distribution, the tightest probabilistic bound on the scene's true shape that can be inferred from the photos. We show how to (1) express this distribution in terms of image measurements, (2) represent it compactly by assigning an occupancy probability to each point in space, and (3) design a stochastic reconstruction algorithm that draws fair samples (i.e., 3D photo hulls) from it.

   Related publications:
  • A Probabilistic Theory of Occupancy and Emptiness, Rahul Bhotika, David J. Fleet, and Kiriakos N. Kutulakos, European Conference on Computer Vision (ECCV 2002), Copenhagen, Denmark, May 2002.
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  • A Probabilistic Theory of Occupancy and Emptiness: Detailed Analysis and Proofs, Rahul Bhotika, David J. Fleet, and Kiriakos N. Kutulakos,Technical Report 753, Computer Science Department, University of Rochester, December 2001.
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  Model-based Tracking of 3D Non-Rigid Shapes in Video

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3D Tracking
  Abstract:

We have developed a linear framework for model-based tracking of nonrigid 3D objects and for acquiring such models from video. 3D motions and deformations are calculated directly from image intensities without information-lossy intermediate results. Measurement uncertainty is quantified and fully propagated through the inverse model to yield posterior mean and/or mode pose estimates. A Bayesian framework manages uncertainty, accommodates priors, and gives confidence measures. We obtained highly accurate and robust closed-form motion estimators by minimizing information loss from non-reversible (inner-product and least-squares) operations, and, when unavoidable, performing such operations with the appropriate error norm. For model acquisition, we can refine a crude or generic model to fit the video subject. Demonstrated uses are 3D tracking, 3D model refinement, and super-resolution texture lifting from low-quality low-resolution video.

   Related publications:
  • Flexible Flow for 3D Non-Rigid Tracking and Shape Recovery, Matthew Brand and Rahul Bhotika, IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, Hawaii, December 2001.
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  • Flexible Flow for 3D Non-Rigid Tracking and Shape Recovery, Matthew Brand and Rahul Bhotika, TR 2001-38, Mitsubishi Electric Resarch Labs (MERL), Cambridge,MA.
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Last edited July 25, 2002::Rahul Bhotika:: bhotikaATcs.rochester.edu