Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Ehret, Thibaud"'
Autor:
Bou, Xavier, Facciolo, Gabriele, von Gioi, Rafael Grompone, Morel, Jean-Michel, Ehret, Thibaud
The goal of this paper is to perform object detection in satellite imagery with only a few examples, thus enabling users to specify any object class with minimal annotation. To this end, we explore recent methods and ideas from open-vocabulary detect
Externí odkaz:
http://arxiv.org/abs/2403.05381
Identifying flood affected areas in remote sensing data is a critical problem in earth observation to analyze flood impact and drive responses. While a number of methods have been proposed in the literature, there are two main limitations in availabl
Externí odkaz:
http://arxiv.org/abs/2403.03671
Radiance fields have been a major breakthrough in the field of inverse rendering, novel view synthesis and 3D modeling of complex scenes from multi-view image collections. Since their introduction, it was shown that they could be extended to other mo
Externí odkaz:
http://arxiv.org/abs/2312.12961
Autor:
Bou, Xavier, Artola, Aitor, Ehret, Thibaud, Facciolo, Gabriele, Morel, Jean-Michel, von Gioi, Rafael Grompone
Detecting relevant changes is a fundamental problem of video surveillance. Because of the high variability of data and the difficulty of properly annotating changes, unsupervised methods dominate the field. Arguably one of the most critical issues to
Externí odkaz:
http://arxiv.org/abs/2307.04159
The new generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (30m). We present here a complete framework to identify CH4 plumes using im
Externí odkaz:
http://arxiv.org/abs/2211.15429
Neural radiance fields, or NeRF, represent a breakthrough in the field of novel view synthesis and 3D modeling of complex scenes from multi-view image collections. Numerous recent works have shown the importance of making NeRF models more robust, by
Externí odkaz:
http://arxiv.org/abs/2206.14938
We introduce the Satellite Neural Radiance Field (Sat-NeRF), a new end-to-end model for learning multi-view satellite photogrammetry in the wild. Sat-NeRF combines some of the latest trends in neural rendering with native satellite camera models, rep
Externí odkaz:
http://arxiv.org/abs/2203.08896
Autor:
Ehret, Thibaud, De Truchis, Aurélien, Mazzolini, Matthieu, Morel, Jean-Michel, d'Aspremont, Alexandre, Lauvaux, Thomas, Duren, Riley, Cusworth, Daniel, Facciolo, Gabriele
Methane (CH4) emissions estimates from top-down studies over oil and gas basins have revealed systematic under-estimation of CH4 emissions in current national inventories. Sparse but extremely large amounts of CH4 from oil and gas production activiti
Externí odkaz:
http://arxiv.org/abs/2110.11832
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images. This new acquisition mode allows for modern image restoration techniques, including multi-frame super-resolution. As the satellite moves du
Externí odkaz:
http://arxiv.org/abs/2102.02301
We propose a self-supervised approach for training multi-frame video denoising networks. These networks predict frame t from a window of frames around t. Our self-supervised approach benefits from the video temporal consistency by penalizing a loss b
Externí odkaz:
http://arxiv.org/abs/2004.06957