Zobrazeno 1 - 10
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pro vyhledávání: '"Chawla, Hemang"'
Transformers have revolutionized deep learning based computer vision with improved performance as well as robustness to natural corruptions and adversarial attacks. Transformers are used predominantly for 2D vision tasks, including image classificati
Externí odkaz:
http://arxiv.org/abs/2312.10529
Spatial scene understanding, including monocular depth estimation, is an important problem in various applications, such as robotics and autonomous driving. While improvements in unsupervised monocular depth estimation have potentially allowed models
Externí odkaz:
http://arxiv.org/abs/2311.02393
Autor:
Iqbal, Haris, Chawla, Hemang, Varma, Arnav, Brouns, Terence, Badar, Ahmed, Arani, Elahe, Zonooz, Bahram
Road infrastructure maintenance inspection is typically a labor-intensive and critical task to ensure the safety of all road users. Existing state-of-the-art techniques in Artificial Intelligence (AI) for object detection and segmentation help automa
Externí odkaz:
http://arxiv.org/abs/2210.03570
Self-supervised monocular depth estimation is a salient task for 3D scene understanding. Learned jointly with monocular ego-motion estimation, several methods have been proposed to predict accurate pixel-wise depth without using labeled data. Neverth
Externí odkaz:
http://arxiv.org/abs/2210.02357
Advances in deep learning have resulted in steady progress in computer vision with improved accuracy on tasks such as object detection and semantic segmentation. Nevertheless, deep neural networks are vulnerable to adversarial attacks, thus presentin
Externí odkaz:
http://arxiv.org/abs/2207.07032
The advent of autonomous driving and advanced driver assistance systems necessitates continuous developments in computer vision for 3D scene understanding. Self-supervised monocular depth estimation, a method for pixel-wise distance estimation of obj
Externí odkaz:
http://arxiv.org/abs/2202.03131
Dense depth estimation is essential to scene-understanding for autonomous driving. However, recent self-supervised approaches on monocular videos suffer from scale-inconsistency across long sequences. Utilizing data from the ubiquitously copresent gl
Externí odkaz:
http://arxiv.org/abs/2103.02451
Spatial scene-understanding, including dense depth and ego-motion estimation, is an important problem in computer vision for autonomous vehicles and advanced driver assistance systems. Thus, it is beneficial to design perception modules that can util
Externí odkaz:
http://arxiv.org/abs/2012.08375
The ability to efficiently utilize crowdsourced visual data carries immense potential for the domains of large scale dynamic mapping and autonomous driving. However, state-of-the-art methods for crowdsourced 3D mapping assume prior knowledge of camer
Externí odkaz:
http://arxiv.org/abs/2007.12918
Autonomous vehicles and driver assistance systems utilize maps of 3D semantic landmarks for improved decision making. However, scaling the mapping process as well as regularly updating such maps come with a huge cost. Crowdsourced mapping of these la
Externí odkaz:
http://arxiv.org/abs/2007.04592