TY - JOUR

T1 - Feature-extraction from all-scale Neighborhoods with Applications to Semantic Segmentation of Point Clouds

AU - Leichter, A.

AU - Werner, Martin

AU - Sester, M.

N1 - Funding information: This work was partially funded by the Federal Ministry of Education and Research, Germany (Bundesministerium für Bildung und Forschung, Förderkennzeichen 01IS17076). We gratefully acknowledge this support. This work was partially funded by the Federal Ministry of Education and Research, Germany (Bundesministerium fur Bildung und Forschung, Forderkennzeichen 01IS17076). We gratefully acknowledge this support.

PY - 2020/8/12

Y1 - 2020/8/12

N2 - Feature extraction from a range of scales is crucial for successful classification of objects of different size in 3D point clouds with varying point density. 3D point clouds have high relevance in application areas such as terrain modelling, building modelling or autonomous driving. A large amount of such data is available but also that these data is subject to investigation in the context of different tasks like segmentation, classification, simultaneous localisation and mapping and others. In this paper, we introduce a novel multiscale approach to recover neighbourhood in unstructured 3D point clouds. Unlike the typical strategy of defining one single scale for the whole dataset or use a single optimised scale for every point, we consider an interval of scales. In this initial work our primary goal is to evaluate the information gain through the usage of the multiscale neighbourhood definition for the calculation of shape features, which are used for point classification. Therefore, we show and discuss empirical results from the application of classical classification models to multiscale features. The unstructured nature of 3D point cloud makes it necessary to recover neighbourhood information before meaningful features can be extracted. This paper proposes the extraction of geometrical features from a range of neighbourhood with different scales, i.e. neighborhood ranges. We investigate the utilisation of the large set of features in combination with feature aggregation/selection algorithms and classical machine learning techniques. We show that the all-scale-approach outperform single scale approaches as well as the approach with an optimised per point selected scale.

AB - Feature extraction from a range of scales is crucial for successful classification of objects of different size in 3D point clouds with varying point density. 3D point clouds have high relevance in application areas such as terrain modelling, building modelling or autonomous driving. A large amount of such data is available but also that these data is subject to investigation in the context of different tasks like segmentation, classification, simultaneous localisation and mapping and others. In this paper, we introduce a novel multiscale approach to recover neighbourhood in unstructured 3D point clouds. Unlike the typical strategy of defining one single scale for the whole dataset or use a single optimised scale for every point, we consider an interval of scales. In this initial work our primary goal is to evaluate the information gain through the usage of the multiscale neighbourhood definition for the calculation of shape features, which are used for point classification. Therefore, we show and discuss empirical results from the application of classical classification models to multiscale features. The unstructured nature of 3D point cloud makes it necessary to recover neighbourhood information before meaningful features can be extracted. This paper proposes the extraction of geometrical features from a range of neighbourhood with different scales, i.e. neighborhood ranges. We investigate the utilisation of the large set of features in combination with feature aggregation/selection algorithms and classical machine learning techniques. We show that the all-scale-approach outperform single scale approaches as well as the approach with an optimised per point selected scale.

UR - http://www.scopus.com/inward/record.url?scp=85091082170&partnerID=8YFLogxK

U2 - 10.5194/isprs-archives-XLIII-B2-2020-263-2020

DO - 10.5194/isprs-archives-XLIII-B2-2020-263-2020

M3 - Conference article

AN - SCOPUS:85091082170

VL - 43

SP - 263

EP - 270

JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

SN - 1682-1750

IS - B2

T2 - 2020 24th ISPRS Congress - Technical Commission II

Y2 - 31 August 2020 through 2 September 2020

ER -