Multi View 3D Object Detection

Multi-view 3D object detection aims to accurately locate and classify objects in 3D space using images from multiple cameras, a crucial task for autonomous driving and robotics. Current research heavily utilizes Bird's-Eye-View (BEV) representations and transformer-based architectures, focusing on improving depth estimation accuracy, handling occlusions and background clutter, and enhancing efficiency for real-time applications. These advancements are driven by the need for robust and computationally efficient perception systems, impacting the development of safer and more reliable autonomous vehicles and other intelligent systems.