Monocular Event Camera

Monocular event cameras, bio-inspired sensors capturing changes in brightness rather than full frames, are revolutionizing computer vision by offering high dynamic range and low motion blur. Current research focuses on developing algorithms for 3D scene reconstruction, human motion capture, and object tracking using these unique data streams, often employing techniques like Gaussian splatting, deep learning-based patch selection, and multi-model fitting. This work is significant because it enables robust and efficient vision systems in challenging environments, with applications ranging from augmented reality and robotics to autonomous driving and human-computer interaction.