Vision-Centric-BEV-Perception
This article offers a detailed examination of vision-centric bird's-eye-view (BEV) perception technologies relevant to autonomous driving. It discusses geometry-based, depth-based, and network-based approaches, including multi-layer perceptron (MLP) and transformer-based methods, and their use in object detection and semantic segmentation. The survey includes discussions on datasets, benchmarking outcomes, and historical technological developments. It also addresses multi-task learning and fusion strategies, emphasizing advances in multi-modality fusion for improved 3D object detection, providing a valuable insight into current BEV perception technologies.
