Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Pierluigi Zama Ramirez"'
Autor:
Adriano Cardace, Andrea Conti, Pierluigi Zama Ramirez, Riccardo Spezialetti, Samuele Salti, Luigi Di Stefano
Publikováno v:
IEEE Access, Vol 11, Pp 85155-85164 (2023)
LiDAR semantic segmentation is receiving increased attention due to its deployment in autonomous driving applications. As LiDARs come often with other sensors such as RGB cameras, multi-modal approaches for this task have been developed, which howeve
Externí odkaz:
https://doaj.org/article/df809a58092547a1b3a3b365302822a1
Autor:
Daniele De Gregorio, Matteo Poggi, Pierluigi Zama Ramirez, Gianluca Palli, Stefano Mattoccia, Luigi Di Stefano
Publikováno v:
IEEE Access, Vol 9, Pp 119755-119765 (2021)
Self-aware robots rely on depth sensing to interact with the surrounding environment, e.g. to pursue object grasping. Yet, dealing with tiny items, often occurring in industrial robotics scenarios, may represent a challenge due to lack of sensors yie
Externí odkaz:
https://doaj.org/article/ca511c346b7f4aaab7ee7db6014415a6
Autor:
Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
Point cloud classification is a popular task in 3D vision. However, previous works, usually assume that point clouds at test time are obtained with the same procedure or sensor as those at training time. Unsupervised Domain Adaptation (UDA) instead,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e736783150b564a19c69ce05823a8ea9
http://arxiv.org/abs/2210.08226
http://arxiv.org/abs/2210.08226
Although deep neural networks have achieved remarkable results for the task of semantic segmentation, they usually fail to generalize towards new domains, especially when performing synthetic-to-real adaptation. Such domain shift is particularly noti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89750d6cd6b59e868364cdd1c2c12447
https://hdl.handle.net/11585/864985
https://hdl.handle.net/11585/864985
Autor:
Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcc23533216c37cf73e1c9174b18e5bb
http://arxiv.org/abs/2206.04671
http://arxiv.org/abs/2206.04671
Autor:
Filippo Aleotti, Fabio Tosi, Pierluigi Zama Ramirez, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
We introduce a novel architecture for neural disparity refinement aimed at facilitating deployment of 3D computer vision on cheap and widespread consumer devices, such as mobile phones. Our approach relies on a continuous formulation that enables to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe9637fbb2cd26e21029e4355f88ed48
http://arxiv.org/abs/2110.15367
http://arxiv.org/abs/2110.15367
Autor:
Fabio Tosi, Pierluigi Zama Ramirez, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
We address the problem of registering synchronized color (RGB) and multi-spectral (MS) images featuring very different resolution by solving stereo matching correspondences. Purposely, we introduce a novel RGB-MS dataset framing 13 different scenes i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0c2439fb40beefbb2aabeb5059120f4
https://hdl.handle.net/11585/895292
https://hdl.handle.net/11585/895292
Although recent semantic segmentation methods have made remarkable progress, they still rely on large amounts of annotated training data, which are often infeasible to collect in the autonomous driving scenario. Previous works usually tackle this iss
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::684f51bf87ee071c9013693447c13a3b
Autor:
Luigi Lella, Luigi Di Stefano, Luca De Luigi, Claudio Paternesi, Pierluigi Zama Ramirez, Daniele De Gregorio
Publikováno v:
AIVR
Availability of a few, large-size, annotated datasets, like ImageNet, Pascal VOC and COCO, has lead deep learning to revolutionize computer vision research by achieving astonishing results in several vision tasks. We argue that new tools to facilitat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc32b6e24b6061c3be4bf3dd9478bf00
https://hdl.handle.net/11585/806564
https://hdl.handle.net/11585/806564
Autor:
Samuele Salti, Filippo Aleotti, Fabio Tosi, Pierluigi Zama Ramirez, Stefano Mattoccia, Matteo Poggi, Luigi Di Stefano
Publikováno v:
CVPR
Whole understanding of the surroundings is paramount to autonomous systems. Recent works have shown that deep neural networks can learn geometry (depth) and motion (optical flow) from a monocular video without any explicit supervision from ground tru
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d47a9684c58d29a0a6c43306153bd89