A Data augmentation method for scanning from different secondary positions

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Computer Vision

A Data augmentation method for scanning from different secondary positions

Simulated LiDAR Repositioning: a novel point cloud data augmentation method

We define criteria for selecting valid secondary positions, and then estimate which points from the original pointcloud would be acquired by a scanner from these positions.

We validate the method using synthetic scenes, and examine how the similarity of generated point clouds depends on scanner distance, occlusion, and angular resolution.

We show that the method is more accurate at short distances, and that having a high scanner resolution for the original point clouds has a strong impact on the similarity of generated point clouds.

We also demonstrate how the method can be applied to natural scene statistics : in particular, we apply our method to reposition the scanner horizontally and vertically, separately consider points belonging to the ground and to non-ground objects, and describe the impact on the distributions of distances to these two classes of points.

Authors

Xavier Morin-Duchesne, Michael S Langer

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