END-TO-END DEPTH from MOTION with STABILIZED MONOCULAR VIDEOS

C. Pinard; L. Chevalley; A. Manzanera; D. Filliat

doi:10.5194/isprs-annals-IV-2-W3-67-2017

END-TO-END DEPTH from MOTION with STABILIZED MONOCULAR VIDEOS

C. Pinard
, L. Chevalley
, A. Manzanera
, D. Filliat

Parrot
ENSTA ParisTech

Résultats de recherche: Contribution à un journal › Article de conférence › Revue par des pairs

Résumé

We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid scenes. Unlike most navigation datasets, the lack of rotation implies an easier structure from motion problem which can be leveraged for different kinds of tasks such as depth inference and obstacle avoidance. We also propose an architecture for end-to-end depth inference with a fully convolutional network. Results show that although tied to camera inner parameters, the problem is locally solvable and leads to good quality depth prediction.

langue originale	Anglais
Pages (de - à)	67-74
Nombre de pages	8
journal	ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume	4
Numéro de publication	2W3
Les DOIs	https://doi.org/10.5194/isprs-annals-IV-2-W3-67-2017
état	Publié - 18 août 2017
Modification externe	Oui
Evénement	4th ISPRS International Conference on Unmanned Aerial Vehicles in Geomatics, UAV-g 2017 - Bonn, Allemagne Durée: 4 sept. 2017 → 7 sept. 2017

Accès au document

10.5194/isprs-annals-IV-2-W3-67-2017

Contient cette citation

@article{6917b7e6f3c845be80498e2f9bcb9afb,

title = "END-TO-END DEPTH from MOTION with STABILIZED MONOCULAR VIDEOS",

abstract = "We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid scenes. Unlike most navigation datasets, the lack of rotation implies an easier structure from motion problem which can be leveraged for different kinds of tasks such as depth inference and obstacle avoidance. We also propose an architecture for end-to-end depth inference with a fully convolutional network. Results show that although tied to camera inner parameters, the problem is locally solvable and leads to good quality depth prediction.",

keywords = "Dataset, Deep Learning, Depth from Motion, End-to-end, Monocular, Navigation",

author = "C. Pinard and L. Chevalley and A. Manzanera and D. Filliat",

note = "Publisher Copyright: {\textcopyright} Authors 2017.; 4th ISPRS International Conference on Unmanned Aerial Vehicles in Geomatics, UAV-g 2017 ; Conference date: 04-09-2017 Through 07-09-2017",

year = "2017",

month = aug,

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doi = "10.5194/isprs-annals-IV-2-W3-67-2017",

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TY - JOUR

T1 - END-TO-END DEPTH from MOTION with STABILIZED MONOCULAR VIDEOS

AU - Pinard, C.

AU - Chevalley, L.

AU - Manzanera, A.

AU - Filliat, D.

N1 - Publisher Copyright: © Authors 2017.

PY - 2017/8/18

Y1 - 2017/8/18

N2 - We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid scenes. Unlike most navigation datasets, the lack of rotation implies an easier structure from motion problem which can be leveraged for different kinds of tasks such as depth inference and obstacle avoidance. We also propose an architecture for end-to-end depth inference with a fully convolutional network. Results show that although tied to camera inner parameters, the problem is locally solvable and leads to good quality depth prediction.

AB - We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid scenes. Unlike most navigation datasets, the lack of rotation implies an easier structure from motion problem which can be leveraged for different kinds of tasks such as depth inference and obstacle avoidance. We also propose an architecture for end-to-end depth inference with a fully convolutional network. Results show that although tied to camera inner parameters, the problem is locally solvable and leads to good quality depth prediction.

KW - Dataset

KW - Deep Learning

KW - Depth from Motion

KW - End-to-end

KW - Monocular

KW - Navigation

U2 - 10.5194/isprs-annals-IV-2-W3-67-2017

DO - 10.5194/isprs-annals-IV-2-W3-67-2017

M3 - Conference article

AN - SCOPUS:85030253114

SN - 2194-9042

VL - 4

SP - 67

EP - 74

JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

IS - 2W3

T2 - 4th ISPRS International Conference on Unmanned Aerial Vehicles in Geomatics, UAV-g 2017

Y2 - 4 September 2017 through 7 September 2017

ER -

END-TO-END DEPTH from MOTION with STABILIZED MONOCULAR VIDEOS

Résumé

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