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Cheaper by the Dozen: Group Annotation of 3D Data

1,400 views · Published 27 September 2014 · 0:31 · Indexed 20 September 2026

Channel: Association for Computing Machinery (ACM) · 2014 · Science & Technology

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Cheaper by the Dozen: Group Annotation of 3D Data
Aleksey Boyko, Thomas Funkhouser

Abstract:
This paper proposes a group annotation approach to interactive \ 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 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 \ 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 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  \ 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.

ACM DL: http://dl.acm.org/citation.cfm?id=2647418
DOI: http://dx.doi.org/10.1145/2642918.2647418

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