Camera Based 3D Object

Camera-based 3D object detection aims to accurately identify and locate objects in three-dimensional space using only image data from cameras, often multiple cameras. Current research focuses on improving depth estimation accuracy through techniques like stereo vision, multi-view fusion (including Bird's-Eye-View representations), and incorporating temporal information from sequential images. These advancements leverage various model architectures, including transformers and recurrent neural networks, to enhance detection performance and robustness, particularly addressing challenges like long-range detection and adversarial attacks. This field is crucial for autonomous driving and robotics, offering a cost-effective and potentially more versatile alternative to LiDAR-based systems.