J.M.M. Montiel — accepted AI-conference papers

2023

LightDepth: Single-View Depth Self-Supervision from Illumination Decline

Javier Rodríguez-Puigvert, Víctor M. Batlle, J.M.M. Montiel, Ruben Martinez-Cantin, Pascal Fua, Juan D. Tardós +1

ICCV 2023poster

Single-view depth estimation can be remarkably effective if there is enough ground-truth depth data for supervised training. However, there are scenarios, especially in medicine in the case of endoscopies, where such data cannot be obtained. In such cases, multi-view self-supervision and synthetic-t…

Cited by 8 PDF ↗Scholar ↗

2023

Reuse your features: unifying retrieval and feature-metric alignment

Javier Morlana, J.M.M. Montiel

ICRA 2023poster

We propose a compact pipeline to unify all the steps of Visual Localization: image retrieval, candidate re-ranking and initial pose estimation, and camera pose refinement. Our key assumption is that the deep features used for these individual tasks share common characteristics, so we should reuse th…

Cited by 2 Source ↗code ↗Scholar ↗

2022

Photometric single-view dense 3D reconstruction in endoscopy

Víctor M. Batlle, J.M.M. Montiel, Juan D. Tardós

IROS 2022poster

Visual SLAM inside the human body will open the way to computer-assisted navigation in endoscopy. However, due to space limitations, medical endoscopes only provide monocular images, leading to systems lacking true scale. In this paper, we exploit the controlled lighting in colonoscopy to achieve th…

Cited by 15 Source ↗Scholar ↗

2022

Tracking monocular camera pose and deformation for SLAM inside the human body

Juan J. Gómez Rodríguez, J.M.M. Montiel, Juan D. Tardós

IROS 2022poster

Monocular SLAM in deformable scenes will open the way to multiple medical applications like computer-assisted navigation in endoscopy, automatic drug delivery or autonomous robotic surgery. In this paper we propose a novel method to simultaneously track the camera pose and the 3D scene deformation,…

Cited by 18 Source ↗Scholar ↗

2019

ORBSLAM-Atlas: a robust and accurate multi-map system

Richard Elvira, Juan D. Tardós, J.M.M. Montiel

IROS 2019poster

We propose ORBSLAM-Atlas, a system able to handle an unlimited number of disconnected sub-maps, that includes a robust map merging algorithm able to detect submaps with common regions and seamlessly fuse them. The outstanding robustness and accuracy of ORBSLAM are due to its ability to detect wide-b…

Cited by 91 Source ↗Scholar ↗