Cheaper by the Dozen: Group Annotation of 3D Data

This paper proposes a group annotation approach to interac-tive semantic labeling of data and demonstrates the idea in a system for labeling objects in 3D LiDAR scans of a city. In this approach, the system selects a group of objects, predicts a semantic label for it, and highlights it in an interactive dis-play. In response, the user either confirms the predicted label, provides a different label, or indicates that no single label can be assigned to all objects in the group. This sequence of in-teractions repeats until a label has been confirmed for every object in the data set. The main advantage of this approach is that it provides faster interactive labeling rates than alter-native approaches, especially in cases where all labels must be explicitly confirmed by a person. The main challenge is to provide an algorithm that selects groups with many objects all of the same label type arranged in patterns that are quick to recognize, which requires models for predicting object labels and for estimating times for people to recognize objects in groups. We address these challenges by defining an objective function that models the estimated time required to process all unlabeled objects and approximation algorithms to minimize it. Results of user studies suggest that group annotation can be used to label objects in LiDAR scans of cities significantly faster than one-by-one annotation with active learning