Dynamic Reconstruction

Dynamic reconstruction focuses on creating accurate, time-varying 3D models from various data sources, such as X-ray scans, neuromorphic sensors, and video, aiming to capture both spatial and temporal information. Current research emphasizes efficient algorithms, often leveraging neural networks (like NeRFs and Gaussian splatting) or optimization-based approaches, to handle challenges such as sparse data, motion blur, and occlusions. These advancements are significant for applications ranging from medical imaging and robotics (e.g., robot-assisted surgery) to autonomous driving and environmental monitoring, enabling more detailed and accurate understanding of dynamic processes.