1,331 research outputs found
Real-time Monocular Object SLAM
We present a real-time object-based SLAM system that leverages the largest
object database to date. Our approach comprises two main components: 1) a
monocular SLAM algorithm that exploits object rigidity constraints to improve
the map and find its real scale, and 2) a novel object recognition algorithm
based on bags of binary words, which provides live detections with a database
of 500 3D objects. The two components work together and benefit each other: the
SLAM algorithm accumulates information from the observations of the objects,
anchors object features to especial map landmarks and sets constrains on the
optimization. At the same time, objects partially or fully located within the
map are used as a prior to guide the recognition algorithm, achieving higher
recall. We evaluate our proposal on five real environments showing improvements
on the accuracy of the map and efficiency with respect to other
state-of-the-art techniques
Proximity effect in atomic-scaled hybrid superconductor/ferromagnet structures: crucial role of electron spectra
We study the influence of the configuration of the majority and minority spin
subbands of electron spectra on the properties of atomic-scaled
superconductor-ferromagnet S-F-S and F-S-F hybrid structures. At low
temperatures, the S/F/S junction is either a 0 or junction depending on the
energy shift between S and F materials and the anisotropy of the Fermi
surfaces. We found that the spin switch effect in F/S/F system can be reversed
if the minority spin electron spectra in F metal is of the hole-like type
NR-SLAM: Non-Rigid Monocular SLAM
In this paper we present NR-SLAM, a novel non-rigid monocular SLAM system
founded on the combination of a Dynamic Deformation Graph with a Visco-Elastic
deformation model. The former enables our system to represent the dynamics of
the deforming environment as the camera explores, while the later allows us to
model general deformations in a simple way. The presented system is able to
automatically initialize and extend a map modeled by a sparse point cloud in
deforming environments, that is refined with a sliding-window Deformable Bundle
Adjustment. This map serves as base for the estimation of the camera motion and
deformation and enables us to represent arbitrary surface topologies,
overcoming the limitations of previous methods. To assess the performance of
our system in challenging deforming scenarios, we evaluate it in several
representative medical datasets. In our experiments, NR-SLAM outperforms
previous deformable SLAM systems, achieving millimeter reconstruction accuracy
and bringing automated medical intervention closer. For the benefit of the
community, we make the source code public.Comment: 12 pages, 7 figures, submited to the IEEE Transactions on Robotics
(T-RO
LightNeuS: Neural Surface Reconstruction in Endoscopy using Illumination Decline
We propose a new approach to 3D reconstruction from sequences of images
acquired by monocular endoscopes. It is based on two key insights. First,
endoluminal cavities are watertight, a property naturally enforced by modeling
them in terms of a signed distance function. Second, the scene illumination is
variable. It comes from the endoscope's light sources and decays with the
inverse of the squared distance to the surface. To exploit these insights, we
build on NeuS, a neural implicit surface reconstruction technique with an
outstanding capability to learn appearance and a SDF surface model from
multiple views, but currently limited to scenes with static illumination. To
remove this limitation and exploit the relation between pixel brightness and
depth, we modify the NeuS architecture to explicitly account for it and
introduce a calibrated photometric model of the endoscope's camera and light
source. Our method is the first one to produce watertight reconstructions of
whole colon sections. We demonstrate excellent accuracy on phantom imagery.
Remarkably, the watertight prior combined with illumination decline, allows to
complete the reconstruction of unseen portions of the surface with acceptable
accuracy, paving the way to automatic quality assessment of cancer screening
explorations, measuring the global percentage of observed mucosa.Comment: 12 pages, 7 figures, 1 table, submitted to MICCAI 202
Photometric single-view dense 3D reconstruction in endoscopy
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 the first in-vivo 3D reconstruction of the human colon using photometric stereo on a calibrated monocular endoscope. Our method works in a real medical environment, providing both a suitable in-place calibration procedure and a depth estimation technique adapted to the colon's tubular geometry. We validate our method on simulated colonoscopies, obtaining a mean error of 7% on depth estimation, which is below 3 mm on average. Our qualitative results on the EndoMapper dataset show that the method is able to correctly estimate the colon shape in real human colonoscopies, paving the ground for truescale monocular SLAM in endoscopy
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