Publications related to Large Steps in Inverse Rendering of Geometry

Slice-by-Slice versus Fully 3D Reconstruction for Parallel-Beam x-Ray Microtomography

Michaël Unser, Masih Nilchian, Michael Thompson McCann

We present a 3D iterative reconstruction method for microtomography, where iterative methods have previously been limited by their high computational cost. Our preliminary results show that it improves reconstruction quality over the traditional slice-by-s ...

OSA2016

A Primal-dual Framework For Mixtures Of Regularisers

Volkan Cevher, Cosimo Aprile, Quoc Tran Dinh, Luca Baldassarre, Baran Gözcü

Effectively solving many inverse problems in engineering requires to leverage all possible prior information about the structure of the signal to be estimated. This often leads to tackling constrained optimization problems with mixtures of regularizers. Pr ...

2015

On the Use of Transient Information for Static Real-Time Optimization

Dominique Bonvin, Grégory François, Alejandro Gabriel Marchetti

Optimal operation of chemical processes is key for meeting productivity, quality, safety and environmental objectives. Both model-based and data-driven schemes are used to compute optimal operating conditions [1]: - The model-based techniques are intu ...

2015

Point localization in Multi-camera system

Alireza Ghasemi, Soumyabrata Dev

Point localization in multi-camera setups has been widely studied in computer vision. Recently, in finite-resolution camera settings, a consistent and optimal point localization algorithm called SHARP has been proposed, under the assumption of noiseless ca ...

pas d'éditeur2015

Scalable Convex Methods for Phase Retrieval

Volkan Cevher, Alp Yurtsever, Ya-Ping Hsieh

This paper describes scalable convex optimization methods for phase retrieval. The main characteristics of these methods are the cheap per-iteration complexity and the low-memory footprint. With a variant of the original PhaseLift formulation, we first ill ...

2015

Path-following gradient-based decomposition algorithms for separable convex optimization

Quoc Tran Dinh

A new decomposition optimization algorithm, called path-following gradient-based decomposition, is proposed to solve separable convex optimization problems. Unlike path-following Newton methods considered in the literature, this algorithm does not require ...

Springer Verlag2014

Socially-Aware Large-Scale Crowd Forecasting

Alexandre Massoud Alahi, Vignesh Ramanathan

In crowded spaces such as city centers or train stations, human mobility looks complex, but is often influenced only by a few causes. We propose to quantitatively study crowded environments by introducing a dataset of 42 million trajectories collected in t ...

IEEE2014

From Discrete Measurements to Bounded Gradient Estimates: A Look at Some Regularizing Structures

Dominique Bonvin, Grégory François, Gene Bunin

Obtaining a reliable gradient estimate for an unknown function when given only its discrete measurements is a common problem in many engineering disciplines. While there are many approaches to obtaining an estimate of a gradient, obtaining lower and upper ...

American Chemical Society2013

Use of Transient Measurements for Real-Time Optimization via Modifier Adaptation

Dominique Bonvin, Grégory François

Real-time optimization (RTO) methods use measurements to offset the effect of uncertainty and drive the plant to optimality. Explicit RTO schemes, which are characterized by solving a static optimization problem repeatedly, typically require multiple itera ...

2013

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Volkan Cevher, Anastasios Kyrillidis

The convex

\ell_1

-regularized

\log\det

divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the

\ell_1

-regularization is nonsmooth, the

\log\det

objective is n ...

2013

Large Steps in Inverse Rendering of Geometry

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Slice-by-Slice versus Fully 3D Reconstruction for Parallel-Beam x-Ray Microtomography

A Primal-dual Framework For Mixtures Of Regularisers

On the Use of Transient Information for Static Real-Time Optimization

Point localization in Multi-camera system

Scalable Convex Methods for Phase Retrieval

Path-following gradient-based decomposition algorithms for separable convex optimization

Socially-Aware Large-Scale Crowd Forecasting

From Discrete Measurements to Bounded Gradient Estimates: A Look at Some Regularizing Structures

Use of Transient Measurements for Real-Time Optimization via Modifier Adaptation

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

A Primal-dual Framework For Mixtures Of Regularisers

Point localization in Multi-camera system

Path-following gradient-based decomposition algorithms for separable convex optimization

Use of Transient Measurements for Real-Time Optimization via Modifier Adaptation

Slice-by-Slice versus Fully 3D Reconstruction for Parallel-Beam x-Ray Microtomography

Scalable Convex Methods for Phase Retrieval

On the Use of Transient Information for Static Real-Time Optimization

From Discrete Measurements to Bounded Gradient Estimates: A Look at Some Regularizing Structures

Socially-Aware Large-Scale Crowd Forecasting

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection