This paper proposes a group annotation approach to interactive semantic labeling of data, and it 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 display. 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 interactions repeats itself until a label has been confirmed or provided for every object in the data set. The main advantage of this approach is that it provides faster interactive labeling rates than alternative 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 a classifier for prediction of new object labels and a model for human recognition of 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.

@inproceedings{Boyko:2014:CBT,
   author = "Aleksey Boyko and Thomas Funkhouser",
   title = "Cheaper by the Dozen: Group Annotation of {3D} Data",
   booktitle = "UIST",
   year = "2014",
   month = oct
}