Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Juil Sock"'
Publikováno v:
Sensors, Vol 16, Iss 6, p 933 (2016)
LiDAR and cameras have been broadly utilized in computer vision and autonomous vehicle applications. However, in order to convert data between the local coordinate systems, we must estimate the rigid body transformation between the sensors. In this p
Externí odkaz:
https://doaj.org/article/6e863a45e1154da8bedde1d1540caeff
Publikováno v:
3DV
Most successful approaches to estimate the 6D pose of an object typically train a neural network by supervising the learning with annotated poses in real world images. These annotations are generally expensive to obtain and a common workaround is to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76e849ca0fbd01f005665046c4f545ae
https://doi.org/10.1109/3dv50981.2020.00039
https://doi.org/10.1109/3dv50981.2020.00039
Publikováno v:
IROS
In this work, we explore how a strategic selection of camera movements can facilitate the task of 6D multi-object pose estimation in cluttered scenarios while respecting real-world constraints such as time and distance travelled, important in robotic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3fba1f4d852cebe8b12a4293a67c4806
https://doi.org/10.1109/iros45743.2020.9340842
https://doi.org/10.1109/iros45743.2020.9340842
Object pose recovery has gained increasing attention in the computer vision field as it has become an important problem in rapidly evolving technological areas related to autonomous driving, robotics, and augmented reality. Existing review-related st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2be64d657e79ae969e992626ebf4e03e
Publikováno v:
RGB-D Image Analysis and Processing ISBN: 9783030286026
Interest in estimating the 6D pose, i.e. 3D locations and rotations, of an object of interest has emerged since its promising applications in fields such as robotics and augmented reality. To recover poses from objects that have been seen in advance,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::795dd7628460b3fae9febd07a400b8ae
https://doi.org/10.1007/978-3-030-28603-3_11
https://doi.org/10.1007/978-3-030-28603-3_11
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 32
There is growing need for robots that can interact with people in everyday situations. For service robots, it is not reasonable to assume that one can pre-program all object categories. Instead, apart from learning from a batch of labelled training d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3eacdeeafac780bba14a23558616f520
https://doi.org/10.1609/aaai.v32i1.11319
https://doi.org/10.1609/aaai.v32i1.11319
Autor:
Caner Sahin, Vassileios Balntas, Juil Sock, Tae-Kyun Kim, Rigas Kouskouridas, Andreas Doumanoglou
Publikováno v:
ICCV
In this paper we examine the effects of using object poses as guidance to learning robust features for 3D object pose estimation. Previous works have focused on learning feature embeddings based on metric learning with triplet comparisons and rely on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e50cea8cbae3af458ac07cc6db16835
https://doi.org/10.1109/iccv.2017.416
https://doi.org/10.1109/iccv.2017.416
Publikováno v:
ICCV Workshops
Recovering object pose in a crowd is a challenging task due to severe occlusions and clutters. In active scenario, whenever an observer fails to recover the poses of objects from the current view point, the observer is able to determine the next view
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc03ca815964335331e83db693ac3c8b
https://doi.org/10.1109/iccvw.2017.260
https://doi.org/10.1109/iccvw.2017.260
Publikováno v:
Sensors; Volume 16; Issue 6; Pages: 933
Sensors, Vol 16, Iss 6, p 933 (2016)
Sensors (Basel, Switzerland)
SENSORS(16): 6
Sensors, Vol 16, Iss 6, p 933 (2016)
Sensors (Basel, Switzerland)
SENSORS(16): 6
LiDAR and cameras have been broadly utilized in computer vision and autonomous vehicle applications. However, in order to convert data between the local coordinate systems, we must estimate the rigid body transformation between the sensors. In this p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce0739a2be572335f5b518affbb78a0c
Publikováno v:
ICRA
Estimating the traversability of rough terrain is a critical task for an outdoor mobile robot. While classifying structured environment can be learned from large number of training data, it is an extremely difficult task to learn and estimate travers
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::060357c1766709c6aa2e9e4baaa8bf73
https://doi.org/10.1109/icra.2016.7487782
https://doi.org/10.1109/icra.2016.7487782