https://zhaoylee.github.io/Blogs/tags/%E9%A2%9D%E5%A4%96%E7%9A%84%E6%A8%A1%E5%9E%8B/ Recent content in 额外的模型 on zhaoyli's Blog Hugo zh-cn [©2024 zhaoyli&rsquo;s Blog] https://zhaoylee.github.io/) Mon, 16 Mar 2026 01:45:51 +0000 https://zhaoylee.github.io/Blogs/posts/centernet-based/monoct-overcoming-monocular-3d-detection-domain-shift-with-consistent-teacher-models/ Thu, 12 Mar 2026 10:25:13 +0800 https://zhaoylee.github.io/Blogs/posts/centernet-based/monoct-overcoming-monocular-3d-detection-domain-shift-with-consistent-teacher-models/ <hr> <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">GitHub 链接</a><br> <strong>📄 论文题目</strong>：<a href="https://arxiv.org/abs/2503.13743">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>