BEVDet
BEVDet is a deep learning framework designed for 3D object detection using bird’s-eye view representations. It is primarily used in autonomous driving systems to improve the perception of surrounding environments from multi-camera setups.
3D object detection
SECOND (sparse convolutional object detection)
SECOND (Sparse Convolutional Object Detection) is a deep learning framework designed for 3D object detection using sparse convolutional neural networks. It efficiently processes point cloud data, commonly obtained from LiDAR sensors, to detect and localize objects in three-dimensional space.
PV-RCNN (point-voxel RCNN for 3D detection)
PV-RCNN is a 3D object detection framework that integrates point-based and voxel-based features to improve accuracy in tasks such as autonomous driving. It uses a novel point-voxel feature set abstraction to enhance perception from LiDAR data.