https://vilab.team/tag/%E8%A7%92%E5%BA%A6%E4%BC%B0%E8%AE%A1/ 角度估计 Hugo Blox Builder (https://hugoblox.com)en-usTue, 10 Mar 2026 00:00:00 +0000 https://vilab.team/media/icon_hu2896232876136423579.png https://vilab.team/tag/%E8%A7%92%E5%BA%A6%E4%BC%B0%E8%AE%A1/ https://vilab.team/publication/rio-detr-detr-for-real-time-oriented-object-detection/ Tue, 10 Mar 2026 00:00:00 +0000 https://vilab.team/publication/rio-detr-detr-for-real-time-oriented-object-detection/ <p>本文提出RiO-DETR，一种面向实时旋转目标检测的DETR框架。针对方向语义依赖、角度周期性和搜索空间扩大等挑战，设计了内容驱动的角度估计、旋转校正正交注意力、解耦周期细化以及定向密集O2O机制，在保持实时效率的同时提升角度收敛速度与检测精度。在DOTA-1.0、DIOR-R和FAIR-1M-2.0上的实验表明，该方法实现了新的速度-精度平衡。</p> https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/ Tue, 10 Mar 2026 00:00:00 +0000 https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/ <p>热烈祝贺胡张驰同学！论文《RiO-DETR: DETR for Real-time Oriented Object Detection》已发表在 ECCV 2026 Oral。</p> <h2 id="rio-detr-detr-for-real-time-oriented-object-detection">RiO-DETR: DETR for Real-time Oriented Object Detection</h2> <p><strong>祝贺胡张驰同学！该论文已发表在 ECCV 2026。</strong></p> <p> <figure > <div class="d-flex justify-content-center"> <div class="w-100" ><img alt="RiO-DETR: DETR for Real-time Oriented Object Detection" srcset=" /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/images/paper-01_hu3774487554520496472.webp 400w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/images/paper-01_hu7272795672146682259.webp 760w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/images/paper-01_hu2318532274489056445.webp 1200w" src="https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026-2/images/paper-01_hu3774487554520496472.webp" width="760" height="393" loading="lazy" data-zoomable /></div> </div></figure> </p> <ul> <li><strong>作者：</strong> Zhangchi Hu、Yifan Zhao、Yansong Peng、Wenzhang Sun、Xiangchen Yin、Jie Chen、Peixi Wu、Hebei Li、Xinghao Wang、Dongsheng Jiang、Xiaoyan Sun</li> <li><strong>发表载体：</strong> ECCV 2026</li> <li><strong>发表时间：</strong> 2026年3月10日</li> <li><strong>相关链接：</strong> <a href="https://arxiv.org/abs/2603.09411" target="_blank" rel="noopener">论文链接</a> · <a href="https://arxiv.org/pdf/2603.09411" target="_blank" rel="noopener">PDF</a> · <a href="https://github.com/RicePasteM/RiO-DETR" target="_blank" rel="noopener">代码</a></li> </ul> <h3 id="论文介绍">论文介绍</h3> <p>本文提出RiO-DETR，一种面向实时旋转目标检测的DETR框架。针对方向语义依赖、角度周期性和搜索空间扩大等挑战，设计了内容驱动的角度估计、旋转校正正交注意力、解耦周期细化以及定向密集O2O机制，在保持实时效率的同时提升角度收敛速度与检测精度。在DOTA-1.0、DIOR-R和FAIR-1M-2.0上的实验表明，该方法实现了新的速度-精度平衡。</p> https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/ Tue, 10 Mar 2026 00:00:00 +0000 https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/ <p>热烈祝贺胡张驰同学！论文《RiO-DETR: DETR for Real-time Oriented Object Detection》已发表在 ECCV 2026。</p> <h2 id="rio-detr-detr-for-real-time-oriented-object-detection">RiO-DETR: DETR for Real-time Oriented Object Detection</h2> <p><strong>祝贺胡张驰同学！该论文已发表在 ECCV 2026。</strong></p> <p> <figure > <div class="d-flex justify-content-center"> <div class="w-100" ><img alt="RiO-DETR: DETR for Real-time Oriented Object Detection" srcset=" /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/images/paper-01_hu3774487554520496472.webp 400w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/images/paper-01_hu7272795672146682259.webp 760w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/images/paper-01_hu2318532274489056445.webp 1200w" src="https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-eccv-2026/images/paper-01_hu3774487554520496472.webp" width="760" height="393" loading="lazy" data-zoomable /></div> </div></figure> </p> <ul> <li><strong>作者：</strong> Zhangchi Hu、Yifan Zhao、Yansong Peng、Wenzhang Sun、Xiangchen Yin、Jie Chen、Peixi Wu、Hebei Li、Xinghao Wang、Dongsheng Jiang、Xiaoyan Sun</li> <li><strong>发表载体：</strong> ECCV 2026</li> <li><strong>发表时间：</strong> 2026年3月10日</li> <li><strong>相关链接：</strong> <a href="https://arxiv.org/abs/2603.09411" target="_blank" rel="noopener">论文链接</a> · <a href="https://arxiv.org/pdf/2603.09411" target="_blank" rel="noopener">PDF</a> · <a href="https://github.com/RicePasteM/RiO-DETR" target="_blank" rel="noopener">代码</a></li> </ul> <h3 id="论文介绍">论文介绍</h3> <p>本文提出RiO-DETR，一种面向实时旋转目标检测的DETR框架。针对方向语义依赖、角度周期性和搜索空间扩大等挑战，设计了内容驱动的角度估计、旋转校正正交注意力、解耦周期细化以及定向密集O2O机制，在保持实时效率的同时提升角度收敛速度与检测精度。在DOTA-1.0、DIOR-R和FAIR-1M-2.0上的实验表明，该方法实现了新的速度-精度平衡。</p>