FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY

By Timo Hackel, Jan Dirk Wegner, Konrad Schindler. 2016

373 citations recorded by the source corpus.

Abstract. We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point’s (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.

Subfields: Aerospace Engineering, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/b0ec6c21426c3b3c54b483deb14a6b0fa5b8013c

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Citations in the corpus

Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “Hackel et al. (2016) propose a fast semantic segmentation approach for 3D point clouds with strongly varying densities.”
Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification (2017, methodology): “The Semantic3D12 (Hackel et al., 2016) dataset was acquired by static laser scanners.”
Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark (2017, neutral): “Please refer to (Hackel et al., 2016) for details.”
Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks (2017, methodology): “In urban classiﬁcation [HWS16,CGM09], classifying small objects like cars or street furniture [GKF09] and discriminating between roads and natu-ral terrain become decisive at the smallest possible scale: point level [HWS16].”
Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds (2017, methodology): “[11] use traditional random forest classifiers with 3D features (without color).”
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (2019, methodology): “To extract features from 3D point clouds, traditional approaches usually rely on hand-crafted features [11, 47, 25, 18].”
FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds (2017, background): “Applications of learning on point clouds include shape completion and recognition [54], unmanned autonomous vehicles [34], 3D object detection, recognition and classification [9, 31, 38, 39, 45, 46, 50], contour detection [20], layout inference [16], scene labeling [29], category discovery [57], poi”
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation (2017, background): “Applications of learning on point clouds include shape completion and recognition [57], unmanned autonomous vehicles [36], 3D object detection, recognition and classification [9, 33, 40, 41, 48, 49, 53], contour detection [21], layout inference [18], scene labeling [31], category discovery [60], poi”