Toshiba Cambridge Research Laboratory
Cambridge Research Laboratory > Computer Vision

Computer Vision

CRL’s Computer Vision Group (CVG) conducts cutting-edge research in 3D computer vision and machine learning with the aim to improve the ability of software to understand and interpret visual data. In our work we support Toshiba’s overall mission to create modern technology with a positive impact on society. Our main focus is on fundamental and academically oriented research, and consequently we also have ongoing collaborations with top UK universities.

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Research

 

Online Structure from Motion

The problem of online structure from motion is also known as simultaneous localisation and mapping. It involves a system that estimates a sensor’s pose and the structure of the environment in real-time. We develop methods for visual odometry, loop closure detection and pose graph optimization.

 

Visual Text-to-Speech

In collaboration with CRL’s Speech Technology Group we produced a complete system for expressive visual text-to-speech. Our system is able to producing expressive verbal and visual output in the form of a 'talking head', given an input text and a set of continuous expression weights.

 

Optimization Methods

Energy-minimization methods are ubiquitous in computer vision and related fields. In our work we design efficient low-level optimization strategies at pixel level, and robust optimization methods for 3D vision problems with high complexity.

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Publications

Learning Monocular Visual Odometry with Dense 3D Mapping from Dense 3D Flow
C. Zhao, L. Sun, P. Purkait, T. Ducket and R. Stolkin
IROS, in print arXiv
Descending, Lifting or Smoothing: Secrets of Robust Cost Optimization
C. Zach and G. Bourmaud
ECCV, in print
Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis
V. Estellers, F. R. Schmidt and D. Cremers
3DV, in print
Weakly Supervised Learning of Indoor Geometry by Dual Warping
P. Purkait, U. Bonde and C. Zach
3DV, in print
ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time
R. P. K. Poudel, U. Bonde, S. Liwicki and C. Zach
BMVC, in print arXiv free

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