Zobrazeno 1 - 10
of 7 035
pro vyhledávání: '"Puy A"'
Online object segmentation and tracking in Lidar point clouds enables autonomous agents to understand their surroundings and make safe decisions. Unfortunately, manual annotations for these tasks are prohibitively costly. We tackle this problem with
Externí odkaz:
http://arxiv.org/abs/2409.07887
Autor:
Michele, Björn, Boulch, Alexandre, Vu, Tuan-Hung, Puy, Gilles, Marlet, Renaud, Courty, Nicolas
We tackle the challenging problem of source-free unsupervised domain adaptation (SFUDA) for 3D semantic segmentation. It amounts to performing domain adaptation on an unlabeled target domain without any access to source data; the available informatio
Externí odkaz:
http://arxiv.org/abs/2409.04409
Publikováno v:
Geogaceta, Vol 71 (2022)
Este trabajo presenta nuevos mapas de anomalía magnética y gravimétrica absoluta (Bouguer) del yacimiento polimetálico del Cerro de Almadenes (Otero de Herreros, Segovia). Los datos han sido adquiridos y procesados con equipamiento y técnicas mo
Externí odkaz:
https://doaj.org/article/0c76a5be8eeb488eb0a5cc1f31dcecf5
Autor:
Alberto Santamaría Barragán, Puy Ayarza Arribas, Juan Alcalde, Eduard Saura, David Martí Linares, Imma Palomares, Javier Elez
Publikováno v:
Geogaceta, Vol 71 (2022)
Critical raw materials are essential for the development of our society. However, most shallow ores have already been exploited and only deep targets remain unexplored. This work aims to apply indirect geophysical techniques to the San Finx Sn-W depo
Externí odkaz:
https://doaj.org/article/f9597324d13d4db29319157259a3dfaa
Annotating lidar point clouds for autonomous driving is a notoriously expensive and time-consuming task. In this work, we show that the quality of recent self-supervised lidar scan representations allows a great reduction of the annotation cost. Our
Externí odkaz:
http://arxiv.org/abs/2407.15797
Autor:
Xu, Yihong, Zablocki, Éloi, Boulch, Alexandre, Puy, Gilles, Chen, Mickael, Bartoccioni, Florent, Samet, Nermin, Siméoni, Oriane, Gidaris, Spyros, Vu, Tuan-Hung, Bursuc, Andrei, Valle, Eduardo, Marlet, Renaud, Cord, Matthieu
Motion forecasting is crucial in autonomous driving systems to anticipate the future trajectories of surrounding agents such as pedestrians, vehicles, and traffic signals. In end-to-end forecasting, the model must jointly detect and track from sensor
Externí odkaz:
http://arxiv.org/abs/2406.08113
Autor:
Plombat, Hugo, Puy, Denis
Context. In order to understand the formation of the first stars, which set the transition between the Dark Ages and Cosmic Dawn epochs, it is necessary to provide a detailed description of the physics at work within the first clouds of gas which, du
Externí odkaz:
http://arxiv.org/abs/2404.08479
Autor:
Puy, Gilles, Gidaris, Spyros, Boulch, Alexandre, Siméoni, Oriane, Sautier, Corentin, Pérez, Patrick, Bursuc, Andrei, Marlet, Renaud
Self-supervised image backbones can be used to address complex 2D tasks (e.g., semantic segmentation, object discovery) very efficiently and with little or no downstream supervision. Ideally, 3D backbones for lidar should be able to inherit these pro
Externí odkaz:
http://arxiv.org/abs/2310.17504
We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds. We design a contrastive loss between features of Lidar scans captured in the same scene. Several such approaches have been pro
Externí odkaz:
http://arxiv.org/abs/2310.17281
The recent enthusiasm for open-world vision systems show the high interest of the community to perform perception tasks outside of the closed-vocabulary benchmark setups which have been so popular until now. Being able to discover objects in images/v
Externí odkaz:
http://arxiv.org/abs/2310.12904