LI Kai李锴

After accumulating substantial experience in 3D spatial understanding, this line extends my work towards today's frontier: letting large multimodal models reason about 3D structure directly, without camera calibration or dense views — and, building on that foundation, using reinforcement learning to keep sparse-view indoor reconstruction consistent across viewpoints.在积累了丰富的三维空间理解经验之后，这一部分把工作拓展到当下最前沿的方向： 让多模态大模型在没有相机参数、没有密集视图的条件下直接推理三维结构；并在此基础上， 尝试用强化学习让稀疏视角下的室内场景重建保持跨视角一致。

My PhD research targets building extraction under off-nadir satellite photography. I first proposed the concept of the Offset Token, which brings off-nadir photography into the perceptual scope of large vision models. Around this concept I built a complete loop — vectorised building extraction, positional repair between historical vector maps and updated imagery, efficient optimisation of the offset token, and full decoupling of offset learning.博士期间的研究方向为遥感卫星偏移摄影视角下的建筑物提取。研究主体上， 首先提出了偏移量词元（Offset Token）的概念，将偏移摄影带入大模型的理解视角； 并围绕所提出的偏移量词元概念展开更加细腻的研究，实现了偏移摄影下矢量化的建筑物提取、 历史矢量地图与更新影像之间的位置修复、偏移量词元的高效优化计算、偏移量学习解耦等内容， 完成了「概念提出 → 具象化应用 → 任务解耦与独立 → 底层理论降维」的闭环。