Now showing items 1-9 of 9

    • 3D pose estimation in complex environments 

      Peñate Sánchez, Adrián (Universitat Politècnica de Catalunya, 2017-04-07)
      Doctoral thesis
      Open Access
      Although there has been remarkable progress in the pose estimation literature, there are still a number of limitations when existing algorithms must be applied in everyday applications, especially in uncontrolled environments. ...
    • Depth-aware convolutional neural networks for accurate 3D pose estimation in RGB-D images 

      Porzi, Lorenzo; Peñate Sánchez, Adrián; Ricci, Elisa; Moreno-Noguer, Francesc (2017)
      Conference report
      Open Access
      Most recent approaches to 3D pose estimation from RGB-D images address the problem in a two-stage pipeline. First, they learn a classifier –typically a random forest– to predict the position of each input pixel on the ...
    • Efficient monocular pose estimation for complex 3D models 

      Rubio Romano, Antonio; Villamizar Vergel, Michael Alejandro; Ferraz Colomina, Luis; Peñate Sánchez, Adrián; Ramisa Ayats, Arnau; Simó Serra, Edgar; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc (Institute of Electrical and Electronics Engineers (IEEE), 2015)
      Conference report
      Open Access
      We propose a robust and efficient method to estimate the pose of a camera with respect to complex 3D textured models of the environment that can potentially contain more than 100, 000 points. To tackle this problem we ...
    • Estimación monocular y eficiente de la pose usando modelos 3D complejos 

      Rubio Romano, Antonio; Villamizar Vergel, Michael Alejandro; Ferraz Colomina, Luis; Peñate Sánchez, Adrián; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc (2014)
      Conference report
      Open Access
      El siguiente documento presenta un método robusto y eficiente para estimar la pose de una cámara. El método propuesto asume el conocimiento previo de un modelo 3D del entorno, y compara una nueva imagen de entrada únicamente ...
    • Learning depth-aware deep representations for robotic perception 

      Porzi, Lorenzo; Rota Bulò, Samuel; Peñate Sánchez, Adrián; Ricci, Elisa; Moreno-Noguer, Francesc (Institute of Electrical and Electronics Engineers (IEEE), 2017)
      Article
      Open Access
      Exploiting RGB-D data by means of Convolutional Neural Networks (CNNs) is at the core of a number of robotics applications, including object detection, scene semantic segmentation and grasping. Most existing approaches, ...
    • LETHA: learning from high quality inputs for 3D pose estimation in low quality images 

      Peñate Sánchez, Adrián; Moreno-Noguer, Francesc; Andrade-Cetto, Juan; Fleuret, François (Institute of Electrical and Electronics Engineers (IEEE), 2014)
      Conference report
      Open Access
      We introduce LETHA (Learning on Easy data, Test on Hard), a new learning paradigm consisting of building strong priors from high quality training data, and combining them with discriminative machine learning to deal with ...
    • Matchability prediction for full-search template matching algorithms 

      Peñate Sánchez, Adrián; Porzi, Lorenzo; Moreno-Noguer, Francesc (Institute of Electrical and Electronics Engineers (IEEE), 2015)
      Conference report
      Open Access
      While recent approaches have shown that it is possible to do template matching by exhaustively scanning the parameter space, the resulting algorithms are still quite demanding. In this paper we alleviate the computational ...
    • MSClique: Multiple structure discovery through the maximum weighted clique problem 

      Sanromà Güell, Gerard; Peñate Sánchez, Adrián; Alquézar Mancho, René; Serratosa Casanelles, Francesc; Moreno-Noguer, Francesc; Andrade-Cetto, Juan; González Ballester, Miguel Ángel (2016-01-01)
      Article
      Open Access
      We present a novel approach for feature correspondence and multiple structure discovery in computer vision. In contrast to existing methods, we exploit the fact that point-sets on the same structure usually lie close to ...
    • Real time vehicle recognition: a novel method for road detection 

      Peñate Sánchez, Adrián; Quesada Arencibia, Alexis; Travieso González, Carlos M. (Springer, 2011)
      Conference report
      Open Access
      Knowing the location of the road in an intelligent traffic systems is one of the most used solutions to ease vehicle detection. For this purpose we propose a vehicle recognition algorithm which performs a real time automatic ...