论文标题
价值：针对城市环境的大型基于投票的自动标签
VALUE: Large Scale Voting-based Automatic Labelling for Urban Environments
论文作者
论文摘要
本文提出了一种简单而健壮的方法，用于在大规模的城市环境中自动定位静态3D对象。通过利用合并大量嘈杂但准确本地化的2D图像数据的潜力，我们就恢复的3D信息的鲁棒性和准确性都达到了卓越的性能。该方法基于一个简单的分布式投票模式，该模式可以完全分布并平行于扩展到大规模场景。为了评估该方法，我们从纽约市和旧金山收集了城市规模的数据集，这些数据集由跨越40 km $^2 $面积的近40万张图像组成，并用它来准确恢复交通信号灯的3D位置。我们证明了出色的性能，还表明，随着数据量的增加，解决方案随着时间的推移而提高了质量。
This paper presents a simple and robust method for the automatic localisation of static 3D objects in large-scale urban environments. By exploiting the potential to merge a large volume of noisy but accurately localised 2D image data, we achieve superior performance in terms of both robustness and accuracy of the recovered 3D information. The method is based on a simple distributed voting schema which can be fully distributed and parallelised to scale to large-scale scenarios. To evaluate the method we collected city-scale data sets from New York City and San Francisco consisting of almost 400k images spanning the area of 40 km$^2$ and used it to accurately recover the 3D positions of traffic lights. We demonstrate a robust performance and also show that the solution improves in quality over time as the amount of data increases.
