Feature based approaches for point cloud based geomonitoring

Areal deformation monitoring based on point clouds can be a very valuable alternative to the established point-based monitoring techniques, especially for deformation monitoring of natural scenes. However, despite the increasing use of point clouds to detect and quantify changes of natural structures, several challenges remain unresolved regarding the point cloud-based deformation analysis. In particular, these include the estimation of 3D displacement vector fields, parameterization of deformations, and quantification of error probabilities such as false alarm rate and probability of missed detection.

Several of these challenges are a result of the naive establishing of the point correspondences, which are in traditional methods (e.g., C2C and M3C2 [1]) defined by proximity in the Euclidean space. In this research project, we proposed to rather establish corresponding points based on the proximity in the feature space spanned by local feature descriptors. In particular we have developed and very successfully applied a learned descriptor, based on a convolutional neural network, rather than a hand-crafted one. The displacement vector field resulting from correspondence search in the feature space is noisy due to inherent limitations of the technology producing the point clouds, e.g. due to the sampling pattern of laser scanners, but also due to occlusions, motion, and limited descriptiveness of the feature descriptors.