David Recasens — accepted AI-conference papers

2023

The Drunkard’s Odometry: Estimating Camera Motion in Deforming Scenes

David Recasens, Martin R. Oswald, Marc Pollefeys, Javier Civera

NeurIPS 2023poster

Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2021

Endo-Depth-and-Motion: Reconstruction and Tracking in Endoscopic Videos Using Depth Networks and Photometric Constraints

David Recasens, José Lamarca, José M. Fácil, J. M. M. Montiel, Javier Civera

RA-L 2021

Estimating a scene reconstruction and the camera motion from in-body videos is challenging due to several factors, e.g. the deformation of in-body cavities or the lack of texture. In this paper we present Endo-Depth-and-Motion, a pipeline that estimates the 6-degrees-of-freedom camera pose and dense

Cited by 161 Source ↗code ↗Scholar ↗