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    <title>MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models</title>
    <link>https://zhaoylee.github.io/Blogs_lovelt/posts/monoct-overcoming-monocular-3d-detection-domain-shift-with-consistent-teacher-models/</link>
    <pubDate>Thu, 12 Mar 2026 10:25:13 &#43;0800</pubDate>
    <author>zhaoylee</author>
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<blockquote>
<p><strong>🏛️ 会议/期刊</strong>：ICRA<br>
<strong>📅 发表年份</strong>：2025<br>
<strong>💻 开源代码</strong>：<a href="%e5%a1%ab%e5%86%99%e4%bd%a0%e7%9a%84URL" rel="">GitHub 链接</a><br>
<strong>📄 论文题目</strong>：<a href="https://arxiv.org/abs/2503.13743" target="_blank" rel="noopener noreffer ">MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models</a></p>
</blockquote>
<hr>
<h2 id="0-一句话总结-tldr">0. 一句话总结 (TL;DR)</h2>
<p><em>(这篇论文用什么方法，解决了什么问题，达到了什么效果)</em><br>
MonoCT 提出了一种基于<strong>一致性教师模型（Consistent Teacher）的</strong>半监督自适应框架，通过在目标域（Target Domain）引入伪标签一致性约束，有效解决了单目 3D 检测在不同数据集间迁移时的深度估计偏差问题。</p>]]></description>
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