|
I am a PhD student in computer vision and deep learning focused on 3D reconstruction / camera odometry / 2D-3D flow and depth plus uncertainty estimation in monocular non-rigid dynamic scenes, such as endoscopies. I am pursuing my PhD (starting in January 2021) under the guidance of Prof. Javier Civera at the Robotics Lab in the Universidad de Zaragoza. Research stay at Computer Vision and Geometry Group (CVG) directed by Prof. Marc Pollefeys in ETH Zurich, Switzerland, supervised by Martin. R. Oswald from September 2021 to March 2022. Email / LinkedIn / Google Scholar / Github |
|
|
David Recasens, Martin R. Oswald, Marc Pollefeys, Javier Civera NeurIPS, 2023 project page / arXiv paper / video / GitHub The Drunkard’s Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. And the Drunkard’s Odometry, a novel monocular RGB-D deformable odometry method that breaks down optical flow estimate into rigid-body camera motion and non-rigid scene deformation. |
|
Javier Rodríguez-Puigvert, David Recasens, Javier Civera, Rubén Martínez-Cantín MICCAI, 2022 project page / MICCAI2022 paper / arXiv paper / video demo Deepening for the first time in Bayesian deep networks for single-view depth estimation in colonoscopies. |
|
David Recasens, José Lamarca, José M. Fácil, José María M. Montiel, Javier Civera RA-L and IROS, 2021 project page / RA-L paper / arXiv paper / IROS 2021 video presentation / video demo / GitHub A pipeline that estimates the 6-degrees-of-freedom camera pose and dense 3D scene models from monocular endoscopic videos. |
|
|