no code implementations • 6 Feb 2024 • Elena Aparicio-Esteve, Jesús Ureña, Álvaro Hernández, Daniel Pizarro, David Moltó
This work investigates the use of different state-of-the-art PnP algorithms to localize the receiver in a large space based on four co-planar transmitters and with a distance from transmitters to receiver of 3. 4 m. Encoding techniques are used to permit the simultaneous emission of all the transmitted signals and their processing in the receiver.
no code implementations • 9 Oct 2020 • Shaifali Parashar, Adrien Bartoli, Daniel Pizarro
Step 1 computes the optical flow from correspondences, step 2 reconstructs each 3D point's normal vector using multiple reference images and integrates them to form surfaces with the best reference and step 3 rejects the 3D points that break isometry in their local neighborhood.
no code implementations • 14 Jul 2020 • David Fuentes-Jimenez, Cristina Losada-Gutierrez, David Casillas-Perez, Javier Macias-Guarasa, Roberto Martin-Lopez, Daniel Pizarro, Carlos A. Luna
This paper proposes a DNN-based system that detects multiple people from a single depth image.
no code implementations • 9 Jun 2020 • Juan Manuel Vera-Diaz, Daniel Pizarro, Javier Macias-Guarasa
Time delay estimation is essential in Acoustic Source Localization (ASL) systems.
2 code implementations • 1 Jun 2020 • David Fuentes-Jimenez, Roberto Martin-Lopez, Cristina Losada-Gutierrez, David Casillas-Perez, Javier Macias-Guarasa, Daniel Pizarro, Carlos A. Luna
In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability.
no code implementations • 19 Nov 2018 • David Fuentes-Jimenez, David Casillas-Perez, Daniel Pizarro, Toby Collins, Adrien Bartoli
Compared to previous non-DNN SfT methods, it does not involve numerical optimization at run-time, and is a dense, wide-baseline solution that does not demand, and does not suffer from, feature-based matching.
no code implementations • ECCV 2018 • Shaifali Parashar, Adrien Bartoli, Daniel Pizarro
We present self-calibrating isometric non-rigid structure- from-motion (SCIso-NRSfM), the first method to reconstruct a non-rigid object from at least three monocular images with constant but unknown focal length.
no code implementations • 11 Oct 2017 • David Casillas-Perez, Daniel Pizarro, Manuel Mazo, Adrien Bartoli
It is an important problem as it does not need initial conditions to obtain the unique solution and its the frequent solution that practical algorithms of the state-of-the-art give.
no code implementations • CVPR 2016 • Ajad Chhatkuli, Daniel Pizarro, Toby Collins, Adrien Bartoli
We show for the first time how to construct a Second-Order Cone Programming (SOCP) problem for Non-Rigid Shape-from-Motion (NRSfM) using the Maximum-Depth Heuristic (MDH).
no code implementations • CVPR 2016 • Shaifali Parashar, Daniel Pizarro, Adrien Bartoli
We study Isometric Non-Rigid Shape-from-Motion (Iso-NRSfM): given multiple intrinsically calibrated monocular images, we want to reconstruct the time-varying 3D shape of an object undergoing isometric deformations.
no code implementations • ICCV 2015 • Shaifali Parashar, Daniel Pizarro, Adrien Bartoli, Toby Collins
Volumetric SfT uses the object's full volume to express the deformation constraints and reconstructs the object's surface and interior deformation.
no code implementations • CVPR 2015 • Mathias Gallardo, Daniel Pizarro, Adrien Bartoli, Toby Collins
We focus on isometric deformations, for which 2DSfT is a well-posed problem, and admits an analytical local solution which may be used to initialize nonconvex refinement.
no code implementations • CVPR 2014 • Ajad Chhatkuli, Daniel Pizarro, Adrien Bartoli
It has been recently shown that reconstructing an isometric surface from a single 2D input image matched to a 3D template was a well-posed problem.