An Approach to Extract Moving Objects From MLS Data Using a Volumetric Background Representation

Gehrung, J.; Hebel, Marcus; Arens, Michael; Stilla, Uwe

doi:10.5194/isprs-annals-iv-1-w1-107-2017

Cited by 68 publications

(43 citation statements)

References 10 publications

(13 reference statements)

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“…1a, we provide an overview of the entire scene. To some extent, this scene is a representative scenario of the urban area, including rich information of buildings, vehicles, vegetations, ground surfaces, et al Here, the basic point cloud is original acquired by Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB) (Gehrung et al, 2017). The utilized point clouds are acquired by two Velodyne HDL-64E mounted with an angle of 35…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

Sun

Boerner

et al. 2018

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

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ABSTRACT:In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line-and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.

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Section: Resultsmentioning

confidence: 99%

“…Fig. 5 provides sketch about how the two scanners are mounted (Gehrung et al, 2017). The original raw point clouds are also preprocessed by a statistical outlier removal for down-sampling and noise suppressing.…”

Section: Resultsmentioning

confidence: 99%

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

Sun

Boerner

et al. 2018

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

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“…A voxel list updated by conflict search between the known environment and LiDAR measurements is clustered and filtered for object candidates. The theoretical framework behind Octomap has also been used in our previous works, where we showed that the classification of voxels based on voxel statistics leads to the artifacts mentioned above and therefore to an inaccurate approximation of the environment (Gehrung et al, 2016(Gehrung et al, , 2017.…”

Section: Change Detection Using Occupancy Gridsmentioning

confidence: 99%

“…This leads to conflicting observations and therefore to large patches of incorrectly classified voxels. We showed in (Gehrung et al, 2017) that this effect appears when determining the voxel class on volume statistics only, without including information of higher levels such as the local neighborhood (cf. Figure 1(b)).…”

Section: Discretization Based Artifactsmentioning

confidence: 99%

A Voxel-Based Metadata Structure for Change Detection in Point Clouds of Large-Scale Urban Areas

Gehrung

Hebel

Arens

et al. 2018

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

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ABSTRACT:Mobile laser scanning has not only the potential to create detailed representations of urban environments, but also to determine changes up to a very detailed level. An environment representation for change detection in large scale urban environments based on point clouds has drawbacks in terms of memory scalability. Volumes, however, are a promising building block for memory efficient change detection methods. The challenge of working with 3D occupancy grids is that the usual raycasting-based methods applied for their generation lead to artifacts caused by the traversal of unfavorable discretized space. These artifacts have the potential to distort the state of voxels in close proximity to planar structures. In this work we propose a raycasting approach that utilizes knowledge about planar surfaces to completely prevent this kind of artifacts. To demonstrate the capabilities of our approach, a method for the iterative volumetric approximation of point clouds that allows to speed up the raycasting by 36 percent is proposed.

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“…This representation leads to a fast segmentation but it might fail when the scale of the objects in the scene is too different. Gehrung et al (2017) propose to extract moving objects from MLS data by using a probabilistic volumetric representation of the MLS data in order to cluster points between mobile objects and static objects. However this technique can only be used with 3D sensors.…”

mentioning

confidence: 99%

Range-Image: Incorporating Sensor Topology for Lidar Point Cloud Processing

Biasutti¹,

Aujol²,

Brédif³

et al. 2018

photogramm eng remote sensing

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This paper proposes a novel methodology for LiDAR point cloud processing that takes advantage of the implicit topology of various LiDAR sensors to derive 2D images from the point cloud while bringing spatial structure to each point. The interest of such a methodology is then proved by addressing the problems of segmentation and disocclusion of mobile objects in 3D LiDAR scenes acquired via street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle those problems directly in the 3D space. This work promotes an alternative approach by using this 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. Using the image derived from the sensor data by exploiting the sensor topology, a semi-automatic segmentation procedure based on depth histograms is presented. Then, a variational image inpainting technique is introduced to reconstruct the areas that are occluded by objects.Experiments and validation on real data prove the effectiveness of this methodology both in terms of accuracy and speed.

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An Approach to Extract Moving Objects From MLS Data Using a Volumetric Background Representation

Cited by 68 publications

References 10 publications

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

A Voxel-Based Metadata Structure for Change Detection in Point Clouds of Large-Scale Urban Areas

Range-Image: Incorporating Sensor Topology for Lidar Point Cloud Processing

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