报告题目：Detector-free Feature Matching and its Applications in 3D Vision

 个人简介：周晓巍，浙江大学“百人计划”研究员，国家级青年人才项目入选者。研究方向主要为三维视觉及其在混合现实、机器人等领域的应用。代表性工作包括NeuralBody, NeuralRecon, DeepSnake, PVNet, LoFTR等，十余次获得视觉领域顶级会议口头报告，多次入选CVPR最佳论文候选。曾获得浙江省自然科学一等奖，陆增镛CAD&CG高科技奖一等奖，CCF优秀图形开源贡献奖，入选斯坦福大学发布的2021全球前2%顶尖科学家榜单。担任国际顶级期刊IJCV编委、顶级会议CVPR/ICCV领域主席，图形学与混合现实研讨会（GAMES）执行委员会主席，视觉与学习研讨会（VALSE）常务委员，CSIG三维视觉专委会常务委员。

 报告摘要：Local feature matching is a cornerstone for many 3D geometric vision problems such as structure from motion (SfM) and visual localization. Existing SfM and localization systems are mostly built upon detector-based feature matchers that rely on the successful detection of repeatable feature points across multiple views as the first step, which is difficult for texture-poor scenes. In this talk, I will introduce LoFTR, a detector-free approach to local feature matching and how it can be applied to SfM and visual localization, as well as our solution that won the image matching challenge (IMC) 2023.