更便宜:3D数据的组注释

A. Boyko, T. Funkhouser
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引用次数: 15

摘要

本文提出了一种交互式数据语义标注的分组标注方法,并以城市三维激光雷达扫描中物体标注系统为例进行了演示。在这种方法中,系统选择一组对象,预测其语义标签,并在交互式显示中突出显示它。作为响应,用户要么确认预测的标签,要么提供不同的标签,要么指示不能将单个标签分配给组中的所有对象。这个交互序列会重复,直到为数据集中的每个对象确认了一个标签。这种方法的主要优点是,它提供了比其他方法更快的交互标记速率,特别是在所有标签必须由一个人明确确认的情况下。主要的挑战是提供一种算法来选择具有许多相同标签类型的对象的组,这些对象以快速识别的模式排列,这需要预测对象标签和估计人们识别组中对象的时间的模型。我们通过定义一个目标函数来解决这些挑战,该目标函数对处理所有未标记对象所需的估计时间进行建模,并使用近似算法将其最小化。用户研究结果表明,在城市激光雷达扫描中,分组标注比主动学习的逐个标注要快得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cheaper by the dozen: group annotation of 3D data
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.
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