ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 273–280, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-273-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 273–280, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-273-2020

  03 Aug 2020

03 Aug 2020

HIERARCHICAL PROXIMITY-BASED OVER-SEGMENTATION OF 3-D POINT CLOUDS FOR EFFICIENT GRAPH FEATURE DETECTION

Z. Shtain and S. Filin Z. Shtain and S. Filin
  • Mapping and Geo-Information Engineering, Technion – Israel Institute of Technology, Haifa, Israel

Keywords: Segmentation, Point clouds, Ball-tree, Graph-based clustering, Feature extraction

Abstract. Point cloud simplification is empowered by the definition of similarity metrics which we aim to identify homogeneous regions within the point-cloud. Nonetheless, the variety of shapes and clutter in natural scenes, along with the significant resolution variations, occlusions, and noise, contribute to inconsistencies in the geometric properties, thereby making the homogeneity measurement challenging. Thus, the objective of this paper is to develop a point-cloud simplification model by means of data segmentation and to extract information in a better-suited way. The literature shows that most approaches either apply volumetric data strategies and/or resort to simplified planar geometries, which relate to only part of the entities found within a natural scene. To provide a more general strategy, we propose a proximity-based approach that allows an efficient and reliable surface characterization with no limitation on the number or shape of the primitives which in turn, enables detecting free-form objects. To achieve this, a local, computationally efficient and scalable metric is developed, which captures resolution variation and allows for short processing time. Our proposed scheme is demonstrated on datasets featuring a variety of surface types and characteristics. Experiments show high precision rates while exhibiting robustness to the varying resolution, texture, and occlusions that exist within the sets.