MonoCLUE : Object-Aware Clustering Enhances Monocular 3D Object Detection

The introduction of MonoCLUE marks a significant advancement in monocular 3D object detection, a critical area for autonomous driving technology. Traditional methods face challenges such as ill-posed depth perception and limited field of view, leading to reduced accuracy, especially in occluded scenes. MonoCLUE tackles these issues by employing K-means clustering to capture distinct object-level features, which improves the detection of partially visible objects. Additionally, it constructs a generalized scene memory that aggregates these features across images, ensuring consistent representations that enhance detection stability across various environments. This innovative approach not only improves the robustness of monocular 3D detection under challenging conditions but also achieves state-of-the-art performance, positioning MonoCLUE as a pivotal development in the ongoing evolution of autonomous driving systems.