Feature relevance assessment for the semantic interpretation of 3D point cloud data

Weinmann, Martin; Jutzi, Boris; Mallet, Clément

doi:10.5194/isprsannals-ii-5-w2-313-2013

Cited by 168 publications

(136 citation statements)

References 29 publications

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“…The latter typically involve shape measures represented by a single value which specifies one single property based on characteristics within the local neighborhood (West et al, 2004;Jutzi and Gross, 2009;Mallet et al, 2011;Weinmann et al, 2013;Guo et al, 2015). Such features are to some degree interpretable as they describe fundamental geometric properties of the local neighborhood.…”

Section: Feature Extractionmentioning

confidence: 99%

3D Semantic labeling of ALS point clouds by exploiting multi-scale, multi-type neighborhoods for feature extraction

Blomley

Jutzi

Weinmann

2016

GEOBIA 2016: Solutions and Synergies

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ABSTRACT:The semantic labeling of 3D point clouds acquired via airborne laser scanning typically relies on the use of geometric features. In this paper, we present a framework considering complementary types of geometric features extracted from multi-scale, multi-type neighborhoods to describe (i) the local 3D structure for neighborhoods of different scale and type and (ii) how the local 3D structure behaves across different scales and across different neighborhood types. The derived features are provided as input for several classifiers with different learning principles in order to show the potential and limitations of the proposed geometric features with respect to the classification task. To allow a comparison of the performance of our framework to the performance of existing and future approaches, we evaluate our framework on the publicly available dataset provided for the ISPRS benchmark on 3D semantic labeling.

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Section: Feature Extractionmentioning

confidence: 99%

3D Semantic labeling of ALS point clouds by exploiting multi-scale, multi-type neighborhoods for feature extraction

Blomley

Jutzi

Weinmann

2016

GEOBIA 2016: Solutions and Synergies

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“…Point cloud (or mesh) classification is gaining interest and becoming a very active field of research (Weinmann et al, 2013;Guo et al, 2014;Niemeyer et al, 2014;Schmidt et al, 2014;Weinmann et al, 2014;Xu et al, 2014;Hackel et al, 2016). The class labelling procedure is normally achieved following three different approaches:…”

Section: Classificationmentioning

confidence: 99%

A Review Of Point Clouds Segmentation and Classification Algorithms

Grilli

Menna

Remondino

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

300

138

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ABSTRACT:Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.

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“…Furthermore, it should be taken into account that the use of many features typically increases the computational burden with respect to processing time and memory consumption and, consequently, it seems desirable to involve methods for feature selection. Regarding the classification of 3D point cloud data, feature selection has for instance been addressed by filter-based feature selection methods evaluating (1) feature-class relations to reason about relevant features and (2) partly also feature-feature relations to remove redundancy [22,40]. Furthermore, wrapper-based feature selection methods interacting with a classifier have been applied [37,41].…”

Section: Semantic Classificationmentioning

confidence: 99%

A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

Weinmann

Mallet³

et al. 2017

Remote Sensing

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Abstract:In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as "tree points" and "other points". The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the "tree points". This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6% are labeled as "tree points". The derived results clearly reveal a semantic classification of high accuracy (up to 90.77%) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h).

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Feature relevance assessment for the semantic interpretation of 3D point cloud data

Cited by 168 publications

References 29 publications

3D Semantic labeling of ALS point clouds by exploiting multi-scale, multi-type neighborhoods for feature extraction

3D Semantic labeling of ALS point clouds by exploiting multi-scale, multi-type neighborhoods for feature extraction

A Review Of Point Clouds Segmentation and Classification Algorithms

A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

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