3D Image Labeling via 2D Projection and Point Cloud Recovery

The system performs preliminary clustering of 3D point clouds into point clusters before labeling, organizing the data in advance to facilitate more efficient processing. This preliminary structuring reduces the time required for subsequent labeling operations while maintaining accuracy.

The system introduces 2D projected images as an intermediary representation between the original 3D point cloud and the final labels. By projecting 3D points to 2D, applying 2D labeling techniques, and then mapping labels back to 3D space, the system leverages成熟的2D labeling algorithms to reduce time consumption while preserving 3D labeling accuracy.

The system systematically transforms 3D point clouds to 2D projected images for labeling, then maps the 2D labels back to 3D space. This dimensionality transformation enables the use of efficient 2D processing while recovering 3D information through careful correspondence maintenance between point clusters and their projected regions.

The system establishes bidirectional correspondence between 3D point clusters and their 2D projected regions. Labels obtained from 2D images are fed back to update the 3D point cluster annotations, ensuring that depth relations and object correspondence are preserved through the transformation cycle.

The system creates a 2D copy or projection of the 3D point cloud data, applies mature 2D automated labeling algorithms to this copy, and then transfers the labels back to the original 3D data. This copying approach allows leveraging well-established 2D algorithms while maintaining 3D labeling quality.

The system replaces direct 3D automated labeling (which lacks mature algorithms) with a substituted approach using 2D projection and labeling. By substituting the labeling domain from 3D to 2D, the system can use proven 2D computer vision algorithms to achieve both speed and accuracy.