3D Semantic Modelling With Label Correction For Extensive Outdoor Scene

Abstract

Semantic point cloud labeling is an important step of 3D urban scene mapping. One doable method is projecting labels from 2D semantic image to 3D point cloud. But the projection accuracy depends on the performance of calibration which cannot be absolutely accurate. In addition, the difference of information dimension between image and point cloud, with the addition of different sensor viewpoints can lead to label error in final 3D semantic results. This paper describes a projection based 3D semantic point cloud building method for extensive outdoor scene, and proposed a segmentation based voting strategy to handle the mislabeling problem in raw projection result. Firstly, based on pre-calibration among sensors, we get semantic point cloud by label projection from semantic image to point cloud. Secondly, we employ a segmentation method to cluster point cloud into different parts which belongs to different objects, this process increases the spatial consistency of semantic label at the object-level. Finally, in each part, we use a voting strategy to filter the label of each point. This strategy was tested in real world urban scene, experiments demonstrate the outstanding performance in decreasing the rate of semantic label error.