Camera Trajectory

Camera trajectory estimation and generation are crucial for various applications, including autonomous driving, 3D scene reconstruction, and filmmaking, aiming to accurately determine or create camera movement paths. Current research focuses on robust methods for estimating camera trajectories from various data sources (e.g., images, videos, events), often employing neural networks (like Gaussian splatting and diffusion models) and optimization techniques (e.g., bundle adjustment, graph-based methods) to handle challenges such as motion blur, occlusions, and dynamic scenes. These advancements significantly impact fields like robotics, computer vision, and virtual/augmented reality by enabling more accurate scene understanding and realistic content generation.