3D Tracking

3D tracking aims to accurately estimate the three-dimensional position and orientation of objects over time, a crucial task in various fields like autonomous driving and robotics. Current research emphasizes robust tracking in challenging scenarios (occlusions, dynamic environments, unseen objects), often employing deep learning architectures such as transformers and neural networks, along with techniques like Gaussian splatting and Kalman filtering for improved accuracy and efficiency. These advancements are driving significant improvements in applications ranging from surgical navigation and augmented reality to autonomous vehicle perception and human-computer interaction.