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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.|
|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|
|Pages:||273 - 281|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||International Conference on 3D Vision|
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