How to judge the length of a car by 3D point cloud labeling?

1, 3D point cloud target detection, 3D point cloud target detection needs standard target point cloud or standard point cloud features to describe the vector; Find the point cloud block with the highest similarity to the target point cloud from the point cloud data collected in real time.

2, 3D point cloud key points, 3D point cloud key points can obtain stable and differentiated point sets by defining detection standards. Technically, the number of key points in 3D point cloud is much less than the original point cloud data, and the key point technology has also become a very key technology in 3D information processing.

3. Semantic segmentation of 3D point cloud. Semantic segmentation of three-dimensional point clouds has been applied in many fields, such as autonomous driving, robots and so on, and has become the key to understanding the scene.

4.2D3D fusion labeling, that is, labeling the image data collected by 2D and 3D sensors at the same time, and establishing the relationship. 2D camera data and 3D lidar point cloud data are fused, and the labeler uses visual information and depth information to create more accurate annotation. The autopilot model enhances its visual and radar perception capabilities.