@inproceedings{73860d4f43f64431b23c9a34156ad016,
title = "Learning Hierarchical Semantic Segmentations of LIDAR Data",
abstract = "This paper investigates a method for semantic segmentation of small objects in terrestrial LIDAR scans in urban environments. The core research contribution is a hierarchical segmentation algorithm where potential merges between segments are prioritized by a learned affinity function and constrained to occur only if they achieve a significantly high object classification probability. This approach provides a way to integrate a learned shape-prior (the object classifier) into a search for the best semantic segmentation in a fast and practical algorithm. Experiments with LIDAR scans collected by Google Street View cars throughout ∼ 100 city blocks of New York City show that the algorithm provides better segmentations and classifications than simple alternatives for cars, vans, traffic lights, and street lights.",
keywords = "Google, Image segmentation, Laser radar, Semantics, Shape, Three-dimensional displays, Training",
author = "David Dohan and Brian Matejek and Thomas Funkhouser",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 International Conference on 3D Vision, 3DV 2015 ; Conference date: 19-10-2015 Through 22-10-2015",
year = "2015",
month = nov,
day = "20",
doi = "10.1109/3DV.2015.38",
language = "English (US)",
series = "Proceedings - 2015 International Conference on 3D Vision, 3DV 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "273--281",
editor = "Michael Brown and Jana Kosecka and Christian Theobalt",
booktitle = "Proceedings - 2015 International Conference on 3D Vision, 3DV 2015",
address = "United States",
}