Motion Estimation
Motion estimation aims to determine the movement of objects or cameras within a scene, a crucial task across diverse fields like robotics, medical imaging, and video processing. Current research emphasizes improving accuracy and robustness, particularly in challenging scenarios involving complex motions, occlusions, and limited data, often employing deep learning models (e.g., diffusion models, transformers) and integrating multiple sensor modalities (e.g., LiDAR, IMU, event cameras). These advancements have significant implications for applications ranging from autonomous navigation and surgical robotics to medical image reconstruction and video compression, enabling more accurate and efficient systems.
Papers
Event-Based Visual Odometry on Non-Holonomic Ground Vehicles
Wanting Xu, Si'ao Zhang, Li Cui, Xin Peng, Laurent Kneip
A gradient-based approach to fast and accurate head motion compensation in cone-beam CT
Mareike Thies, Fabian Wagner, Noah Maul, Haijun Yu, Manuela Goldmann, Linda-Sophie Schneider, Mingxuan Gu, Siyuan Mei, Lukas Folle, Alexander Preuhs, Michael Manhart, Andreas Maier