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About

About

Luís Paulo Santos is and Assistant Professor of the Department of Informatics, Universidade do Minho and researcher of CSIG, INESC-TEC. His research area is rendering and global illumination, focusing on algorithms' performance and heterogeneous parallel computing (CPU + GPU + Knights Landing) to reduce convergence tiome towards perceptually correct solutions. He published several papers in the most relevant international fora of Computer Graphics (conferences and journals), and authored a book on Bayesian Monte Carlo Rendering. He nelongs to the Program Committee of several international conferences, having chaired a few of these and organized 6 such events in Portugal.

He has been Vicer Director of the Department, and the Informatics Engineering degree. He was the Director of the Doctoral Programme on Informatics. He integrated the Committe designted by the Rector to install an United Nations University Operational Unit on Electronic Governance in Guimarães, Portugal, and is currently a member of the direction of the unit responsible for the interface between the 2 institutions.

He is Associate Editor of the Computers & Graphics Elsevier journal and President of the Portuguese Group of Computer Graphics, formally the portuguese chapter of Eurographics, for the period of 2017-2018.

Interest
Topics
Details

Details

  • Name

    Luís Paulo Santos
  • Cluster

    Computer Science
  • Role

    Affiliated Researcher
  • Since

    01st January 2017
Publications

2022

Ensemble Metropolis Light Transport

Authors
Bashford-Rogers T.; Santos L.P.; Marnerides D.; Debattista K.;

Publication
ACM Transactions on Graphics

Abstract
This article proposes a Markov Chain Monte Carlo (MCMC) rendering algorithm based on a family of guided transition kernels. The kernels exploit properties of ensembles of light transport paths, which are distributed according to the lighting in the scene, and utilize this information to make informed decisions for guiding local path sampling. Critically, our approach does not require caching distributions in world space, saving time and memory, yet it is able to make guided sampling decisions based on whole paths. We show how this can be implemented efficiently by organizing the paths in each ensemble and designing transition kernels for MCMC rendering based on a carefully chosen subset of paths from the ensemble. This algorithm is easy to parallelize and leads to improvements in variance when rendering a variety of scenes.

2022

Foreword to the special section on Recent Advances in Graphics and Interaction

Authors
Rodrigues, N; Mendes, D; Santos, LP; Bouatouch, K;

Publication
Computers and Graphics (Pergamon)

Abstract

2021

Quantum Tree-Based Planning

Authors
Sequeira, A; Santos, LP; Barbosa, LS;

Publication
IEEE Access

Abstract

2020

Two-level adaptive sampling for illumination integrals using Bayesian Monte Carlo

Authors
Marques, R; Bouville, C; Santos, LP; Bouatouch, K;

Publication
European Association for Computer Graphics - 37th Annual Conference, EUROGRAPHICS 2016 - Short Papers

Abstract
Bayesian Monte Carlo (BMC) is a promising integration technique which considerably broadens the theoretical tools that can be used to maximize and exploit the information produced by sampling, while keeping the fundamental property of data dimension independence of classical Monte Carlo (CMC). Moreover, BMC uses information that is ignored in the CMC method, such as the position of the samples and prior stochastic information about the integrand, which often leads to better integral estimates. Nevertheless, the use of BMC in computer graphics is still in an incipient phase and its application to more evolved and widely used rendering algorithms remains cumbersome. In this article we propose to apply BMC to a two-level adaptive sampling scheme for illumination integrals. We propose an efficient solution for the second level quadrature computation and show that the proposed method outperforms adaptive quasi-Monte Carlo in terms of image error and high frequency noise. © 2016 The Eurographics Association.

2019

Heterogeneous Implementation of a Voronoi Cell-Based SVP Solver

Authors
Falcao, G; Cabeleira, F; Mariano, A; Santos, LP;

Publication
IEEE ACCESS

Abstract
This paper presents a new, heterogeneous CPU+GPU attacks against lattice-based (post-quantum) cryptosystems based on the Shortest Vector Problem (SVP), a central problem in lattice-based cryptanalysis. To the best of our knowledge, this is the first SVP-attack against lattice-based cryptosystems using CPUs and GPUs simultaneously. We show that Voronoi-cell based CPU+GPU attacks, algorithmically improved in previous work, are suitable for the proposed massively parallel platforms. Results show that 1) heterogeneous platforms are useful in this scenario, as they increment the overall memory available in the system (as GPU's memory can be used effectively), a typical bottleneck for Voronoi-cell algorithms, and we have also been able to increase the performance of the algorithm on such a platform, by successfully using the GPU as a co-processor, 2) this attack can be successfully accelerated using conventional GPUs and 3) we can take advantage of multiple GPUs to attack lattice-based cryptosystems. Experimental results show a speedup up to 7.6x for 2 GPUs hosted by an Intel Xeon E5-2695 v2 CPU (12 cores x2 sockets) using only 1 core and gains in the order of 20% for 2 GPUs hosted by the same machine using all 22 CPU threads (2 are reserved for orchestrating the GPUs), compared to single-CPU execution using the entire 24 threads available.

Supervised
thesis

2021

Quantum Reinforcement Learning: Foundations, algorithms, applications

Author
André Manuel Resende Sequeira

Institution
UM

2020

Progressive Sparse Sampling for Physically Based Global Illumination

Author
César Morais Perdigão

Institution
UM

2020

Quantum-enhanced Reinforcement Learning

Author
André Manuel Resende Sequeira

Institution
UM

2019

Mobile Ray-Tracing

Author
Tiago Manuel da Silva Santos

Institution
UM

2019

Progressive Sparse Sampling for Physically Based Global Illumination

Author
César Morais Perdigão

Institution
UP-FCUP