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In this paper we present a novel street scene semantic recognition framework, which takes advantage of 3D point cloud captured by a high definition LiDAR laser scanner. An important problem in object recognition is the need for sufficient labeled training data to learn robust classifiers. We show how to significantly reduce the need for manually labeled training data by reduction of scene complexity...
In this paper we propose a novel street scene semantic parsing framework, which takes advantage of 3D point clouds captured by a high-definition LiDAR laser scanner. Local 3D geometrical features extracted from subsets of point clouds are classified by trained boosted decision trees and then corresponding image segments are labeled with semantic classes e.g. buildings, road, sky etc. In contrast to...
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