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pro vyhledávání: '"Griffiths, David A."'
We consider indoor 3D object detection with respect to a single RGB(-D) frame acquired from a commodity handheld device. We seek to significantly advance the status quo with respect to both data and modeling. First, we establish that existing dataset
Externí odkaz:
http://arxiv.org/abs/2412.04458
Autor:
Bachmann, Roman, Kar, Oğuzhan Fatih, Mizrahi, David, Garjani, Ali, Gao, Mingfei, Griffiths, David, Hu, Jiaming, Dehghan, Afshin, Zamir, Amir
Current multimodal and multitask foundation models like 4M or UnifiedIO show promising results, but in practice their out-of-the-box abilities to accept diverse inputs and perform diverse tasks are limited by the (usually rather small) number of moda
Externí odkaz:
http://arxiv.org/abs/2406.09406
We propose a relighting method for outdoor images. Our method mainly focuses on predicting cast shadows in arbitrary novel lighting directions from a single image while also accounting for shading and global effects such the sun light color and cloud
Externí odkaz:
http://arxiv.org/abs/2204.09341
Autor:
Griffiths, David, author
Publikováno v:
The Future of Ocean Governance and Capacity Development: Essays in Honor of Elisabeth Mann Borgese (1918–2002). :410-415
Autor:
Fournier, Mélanie, author, Griffiths, David, author
Publikováno v:
The Future of Ocean Governance and Capacity Development: Essays in Honor of Elisabeth Mann Borgese (1918–2002). :94-99
In this paper we set out to solve the task of 6-DOF 3D object detection from 2D images, where the only supervision is a geometric representation of the objects we aim to find. In doing so, we remove the need for 6-DOF labels (i.e., position, orientat
Externí odkaz:
http://arxiv.org/abs/2012.01230
Massive semantically labeled datasets are readily available for 2D images, however, are much harder to achieve for 3D scenes. Objects in 3D repositories like ShapeNet are labeled, but regrettably only in isolation, so without context. 3D scenes can b
Externí odkaz:
http://arxiv.org/abs/2004.02693
Publikováno v:
In Geomorphology 1 November 2023 440
Autor:
Griffiths, David, Boehm, Jan
With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional ima
Externí odkaz:
http://arxiv.org/abs/1907.04758
Autor:
Griffiths, David, Boehm, Jan
Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important a
Externí odkaz:
http://arxiv.org/abs/1907.04444