Dimensionality Based Scale Selection in 3d Lidar Point Clouds

Demantké, J.; Mallet, Clément; Nugent, David; Vallet, Bruno

doi:10.5194/isprsarchives-xxxviii-5-w12-97-2011

Cited by 253 publications

(235 citation statements)

References 16 publications

(11 reference statements)

Supporting

Mentioning

213

Contrasting

Order By: Relevance

“…Furthermore, identical values for the scale parameter are typically selected for all points of the 3D point cloud. Recent investigations however revealed that structures related with different classes may favor a different neighborhood size (Weinmann et al, 2015a;Weinmann, 2016) and therefore it seems favorable to allow for more variability by using data-driven approaches for optimal neighborhood size selection (Mitra and Nguyen, 2003;Lalonde et al, 2005;Demantké et al, 2011;Weinmann et al, 2015a).…”

Section: Neighborhood Recoverymentioning

confidence: 99%

Geometric Features and Their Relevance for 3d Point Cloud Classification

Weinmann

Jutzi

Mallet³

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

ABSTRACT:In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (linear structures, planar structures and volumetric structures) as well as a reference labeling with respect to five semantic classes (Wire, Pole/Trunk, Façade, Ground and Vegetation) is available.

show abstract

Section: Neighborhood Recoverymentioning

confidence: 99%

Geometric Features and Their Relevance for 3d Point Cloud Classification

Weinmann

Jutzi

Mallet³

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The methods in this category analyze the point cloud characteristics (Demantke et al, 2011, Weinmann et al, 2015. They consider different properties of the PCA of the neighborhood of each point in order to perform a semantic segmentation.…”

Section: Statistical Segmentationmentioning

confidence: 99%

Disocclusion of 3d Lidar Point Clouds Using Range Images

Biasutti

Aujol

Brédif³

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:This paper proposes a novel framework for the disocclusion of mobile objects in 3D LiDAR scenes aquired via street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle this problem directly in the 3D space. This work promotes an alternative approach by using a 2D range image representation of the 3D point cloud, taking advantage of the fact that the problem of disocclusion has been intensively studied in the 2D image processing community over the past decade. First, the point cloud is turned into a 2D range image by exploiting the sensor's topology. Using the range image, a semi-automatic segmentation procedure based on depth histograms is performed in order to select the occluding object to be removed. A variational image inpainting technique is then used to reconstruct the area occluded by that object. Finally, the range image is unprojected as a 3D point cloud. Experiments on real data prove the effectiveness of this procedure both in terms of accuracy and speed.

show abstract

“…From each planar segment, four features were extracted: the number of points per segment, average residual to the plane, inclination angle and maximal height difference to the surrounding points. The local neighborhood features are based on the observation that the ratio between the eigenvalues of the covariance matrix of the XYZ coordinates of a point's nearest neighbors can represent the shape of the local neighborhood [37]. For example, the relative proportions between these eigenvalues may describe the local neighborhood as being planar, linear or scattered.…”

Section: Feature Extraction From Uav Datamentioning

confidence: 99%

Optimizing Multiple Kernel Learning for the Classification of UAV Data

2016

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAVs) are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from UAVs. The features that are extracted from the point cloud and imagery have different statistical characteristics and can be considered as heterogeneous, which motivates the use of Multiple Kernel Learning (MKL) for classification problems. In this paper, we illustrate the utility of applying MKL for the classification of heterogeneous features obtained from UAV data through a case study of an informal settlement in Kigali, Rwanda. Results indicate that MKL can achieve a classification accuracy of 90.6%, a 5.2% increase over a standard single-kernel Support Vector Machine (SVM). A comparison of seven MKL methods indicates that linearly-weighted kernel combinations based on simple heuristics are competitive with respect to computationally-complex, non-linear kernel combination methods. We further underline the importance of utilizing appropriate feature grouping strategies for MKL, which has not been directly addressed in the literature, and we propose a novel, automated feature grouping method that achieves a high classification accuracy for various MKL methods.

show abstract

Dimensionality Based Scale Selection in 3d Lidar Point Clouds

Cited by 253 publications

References 16 publications

Geometric Features and Their Relevance for 3d Point Cloud Classification

Geometric Features and Their Relevance for 3d Point Cloud Classification

Disocclusion of 3d Lidar Point Clouds Using Range Images

Optimizing Multiple Kernel Learning for the Classification of UAV Data

Contact Info

Product

Resources

About