Object Depth Estimation

Object depth estimation from monocular images aims to infer the three-dimensional distance of objects from a single 2D view, a crucial task for applications like autonomous driving and robotics. Recent research focuses on improving accuracy by leveraging multiple depth cues, such as object height and geometric relationships, and employing techniques like metric learning to enhance feature representation for depth discrimination. These advancements, often integrated into monocular 3D object detection and tracking pipelines, are driven by the need for robust and accurate depth information, ultimately improving the performance of these perception systems. The resulting improvements in depth estimation directly translate to better performance in downstream tasks, such as 3D object detection and tracking.