Maximum Likelihood Visibility Approach

Maximum likelihood visibility approaches aim to improve estimations and predictions by explicitly modeling the probability of an object or point being visible. Current research focuses on developing probabilistic models, often incorporating graph structures or shadow fields, to predict visibility in diverse applications like point cloud video streaming, human pose estimation, and robot navigation. These methods enhance accuracy and robustness in scenarios with occlusions or partial observations, leading to improved performance in tasks such as 3D reconstruction, motion planning, and sensor fusion. The resulting advancements have significant implications for fields requiring accurate perception and decision-making in complex environments.