F2S3: Robustified determination of 3D displacement vector fields using deep learning
| Autor: | Caifa Zhou, Andreas Wieser, Zan Gojcic |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
0211 other engineering and technologies Point cloud 02 engineering and technology outlier detection RANSAC Local feature descriptors 01 natural sciences Deformation monitoring point clouds neural networks displacement vectors Earth and Planetary Sciences (miscellaneous) Engineering (miscellaneous) 021101 geological & geomatics engineering 0105 earth and related environmental sciences Artificial neural network business.industry Deep learning Pattern recognition Modeling and Simulation Anomaly detection Artificial intelligence business Geology |
| Zdroj: | Journal of Applied Geodesy, 14 (2) |
| ISSN: | 1862-9024 1862-9016 |
| Popis: | 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, established deformation analysis approaches for point clouds do not necessarily expose the true 3D changes, because the correspondence between points is typically established naïvely. Recently, approaches to establish the correspondences in the feature space by using local feature descriptors that analyze the geometric peculiarities in the neighborhood of the interest points were proposed. However, the resulting correspondences are noisy and contain a large number of outliers. This impairs the direct applicability of these approaches for deformation monitoring. In this work, we propose Feature to Feature Supervoxel-based Spatial Smoothing (F2S3), a new deformation analysis method for point cloud data. In F2S3 we extend the recently proposed feature-based algorithms with a neural network based outlier detection, capable of classifying the putative pointwise correspondences into inliers and outliers based on the local context extracted from the supervoxels. We demonstrate the proposed method on two data sets, including a real case data set of a landslide located in the Swiss Alps. We show that while the traditional approaches, in this case, greatly underestimate the magnitude of the displacements, our method can correctly estimate the true 3D displacement vectors. Journal of Applied Geodesy, 14 (2) ISSN:1862-9016 ISSN:1862-9024 |
| Databáze: | OpenAIRE |
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