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AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation [article]

Jogendra Nath Kundu, Phani Krishna Uppala, Anuj Pahuja, R. Venkatesh Babu
<span title="2018-06-07">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this work, we propose AdaDepth - an unsupervised domain adaptation strategy for the pixel-wise regression task of monocular depth estimation.  ...  Our unsupervised approach performs competitively with other established approaches on depth estimation tasks and achieves state-of-the-art results in a semi-supervised setting.  ...  We also thank Google India for the travel grant.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.01599v2">arXiv:1803.01599v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gad7mrdeznenppfg4ozjlwnyeu">fatcat:gad7mrdeznenppfg4ozjlwnyeu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930074954/https://arxiv.org/pdf/1803.01599v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1e/1f/1e1fc0c43b74edb3dda01f1250dfad13f3410d61.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.01599v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation

Jogendra Nath Kundu, Phani Krishna Uppala, Anuj Pahuja, R. Venkatesh Babu
<span title="">2018</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</a> </i> &nbsp;
In this work, we propose AdaDepth -an unsupervised domain adaptation strategy for the pixel-wise regression task of monocular depth estimation.  ...  Supervised deep learning methods have shown promising results for the task of monocular depth estimation; but acquiring ground truth is costly, and prone to noise as well as inaccuracies.  ...  We also thank Google India for the travel grant.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2018.00281">doi:10.1109/cvpr.2018.00281</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/KunduUPB18.html">dblp:conf/cvpr/KunduUPB18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wdistqaqhzfpbdm5n7crlivowq">fatcat:wdistqaqhzfpbdm5n7crlivowq</a> </span>
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