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Learning Hierarchical Semantic Segmentations of LIDAR Data

Author(s): Dohan, David; Matejek, Brian; Funkhouser, Thomas

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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.
Publication Date: 2015
Citation: Dohan, David, Brian Matejek, and Thomas Funkhouser. "Learning Hierarchical Semantic Segmentations of LIDAR Data." In International Conference on 3D Vision (2015): pp. 273-281. doi:10.1109/3DV.2015.38
DOI: 10.1109/3DV.2015.38
Pages: 273 - 281
Type of Material: Conference Article
Journal/Proceeding Title: International Conference on 3D Vision
Version: Author's manuscript

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