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Accurate road segmentation is a prerequisite for autonomous driving. Current state-of-the-art methods are mostly based on convolutional neural networks (CNNs). Nevertheless, their good performance is at expense of abundant annotated data and high computational cost. In this work, we address these two issues by a self-paced cross-modality transfer learning framework with efficient projection CNN. To...
In this paper, we aim at detecting vehicles from the point clouds scanned from the urban area. Our detection method consists of a segmentation stage and a classification stage. Prior knowledge for vehicles and urban environment is utilized to help the detection process. Specifically, we incorporate curb detection and removal in the segmentation stage. Moreover, our approach is able to estimate the...
We presented a novel procedure to extract ground road networks from airborne LiDAR data. First point clouds were separated into ground and non-ground parts, and ground roads were to be extracted from ground planes. Then, buildings and trees were distinguished in an energy minimization framework after incorporation of two new features. The separation provided supportive information for later road extractions...
This paper describes an unsupervised approach for efficient extraction of grid-structured urban roads from airborne LIDAR data. Technically, the approach consists of three major components: 1) terrain separation from DSM and classification of ground features, 2) road centerline extraction from generated road candidates images, and 3) completion and verification of complete road networks. A ground-height...
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