Removing non-static objects from 3D laser scan data

Schauer, Johannes; Nüchter, Andreas

doi:10.1016/j.isprsjprs.2018.05.019

Cited by 13 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e registered dataset was imported into the PCL library to eliminate outliers and noise. is library is a large crossplatform open-source C++ programming library containing many algorithms and data structures related to point clouds, including point cloud acquisition, filtering, segmentation, registration, retrieval, feature extraction, recognition, tracking, surface reconstruction, and visualization [25]. e VoxelGrid filter was used for noise reduction.…”

Section: Collection Methods Of 3d Contourmentioning

confidence: 99%

Study on the Accurate Measurement and Quantitative Evaluation Methods of Aggregate Surface Roughness

Guo

Zeng

2021

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

In this work, to quantitatively analyze the roughness of the surfaces of road aggregates, the contact measurement technique and contactless scanning technique were, respectively, used to capture the coordinate data of point clouds on the aggregate surface, which were then used to reconstruct the digital elevation models of aggregate particles. Then, the joint roughness coefficient (JRC) was used as an evaluation index, and the quantitative calculation methods of the two-dimensional (2D) contour line roughness and three-dimensional (3D) contour surface roughness of aggregate particles were, respectively, studied. Finally, the anisotropic characteristics and size effect of the roughness coefficients of aggregates with different lithologies were, respectively, investigated, based on which the practicability of the 3D roughness coefficient index was proven. The results demonstrate that the roughness of a road aggregate surface can be quantitatively described by the point cloud data. The 2D roughness of aggregate profile lines exhibits anisotropy, while the 3D roughness of the aggregate contour surface indicates the size effect. The subtle morphological changes of the surface textures of aggregates can be accurately described by the 3D joint roughness coefficient (JRC3D) calculated by the feature parameter method.

show abstract

Section: Collection Methods Of 3d Contourmentioning

confidence: 99%

Study on the Accurate Measurement and Quantitative Evaluation Methods of Aggregate Surface Roughness

Guo

Zeng

2021

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Methods have also been developed to specifically identify "dynamic" objects in a scene, which is closely related to the change detection problem. In [20], different point clouds are turned into voxel grids, and the identification of "see-through" voxels provides the localization of dynamic points. In this context, "see-through voxels" contain points at a given time, but do not prevent the device from capturing points located behind them at a different time, signifying the removal of their content.…”

Section: Change Detection With 3d Datamentioning

confidence: 99%

Detection of Removed Objects in 3D Meshes Using Up-to-Date Images for Mixed-Reality Applications

Roupin¹,

Fradet²,

Baillard³

et al. 2021

Electronics

View full text Add to dashboard Cite

Precise knowledge of the real environment is a prerequisite for the integration of the real and virtual worlds in mixed-reality applications. However, real-time updating of a real environment model is a costly and difficult process; therefore, hybrid approaches have been developed: An updated world model can be inferred from an offline acquisition of the 3D world, which is then updated online using live image sequences under the condition of developing fast and robust change detection algorithms. Current algorithms are biased toward object insertion and often fail in object removal detection; in an environment where there is uniformity in the background—in color and intensity—the disappearances of foreground objects between the 3D scan of a scene and the capture of several new pictures of said scene are difficult to detect. The novelty of our approach is that we circumvent this issue by focusing on areas of least change in parts of the scene that should be occluded by the foreground. Through experimentation on realistic datasets, we show that this approach results in better detection and localization of removed objects. This technique can be paired with an insertion detection algorithm to provide a complete change detection framework.

show abstract

“…For indoor static scene reconstruction, the related work can be categorized into two research areas: moving objects analysis and 3D reconstruction. Moving objects analysis can be based on images (Tron, Vidal, 2007) or Lidar (Schauer, Nüchter, 2018) points clouds. In this paper, we emphasize on Lidar based indoor reconstruction, so we focus our state of the art on Lidar based methods.…”

Section: Previous Workmentioning

confidence: 99%

“…The Dempster-Shafer theory can be used to improve the combination of volumetric information from the scans (Xiao et al, 2015). Several point based data structures such as Voxel (Andreasson et al, 2007;Schauer, Nüchter, 2018) and OctoMap (Gehrung et al, 2019) have been proposed to improve the performance of the ray tracing method.…”

Section: Moving Objects Analysismentioning

confidence: 99%

Moving Objects Aware Sensor Mesh Fusion for Indoor Reconstruction From a Couple of 2d Lidar Scans

Vallet

Demonceaux³

et al. 2020

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

View full text Add to dashboard Cite

Abstract. Indoor mapping attracts more attention with the development of 2D and 3D camera and Lidar sensor. Lidar systems can provide a very high resolution and accurate point cloud. When aiming to reconstruct the static part of the scene, moving objects should be detected and removed which can prove challenging. This paper proposes a generic method to merge meshes produced from Lidar data that allows to tackle the issues of moving objects removal and static scene reconstruction at once. The method is adapted to a platform collecting point cloud from two Lidar sensors with different scan direction, which will result in different quality. Firstly, a mesh is efficiently produced from each sensor by exploiting its natural topology. Secondly, a visibility analysis is performed to handle occlusions (due to varying viewpoints) and remove moving objects. Then, a boolean optimization allows to select which triangles should be removed from each mesh. Finally, a stitching method is used to connect the selected mesh pieces. Our method is demonstrated on a Navvis M3 (2D laser ranger system) dataset and compared with Poisson and Delaunay based reconstruction methods.

show abstract

Removing non-static objects from 3D laser scan data

Cited by 13 publications

References 14 publications

Study on the Accurate Measurement and Quantitative Evaluation Methods of Aggregate Surface Roughness

Study on the Accurate Measurement and Quantitative Evaluation Methods of Aggregate Surface Roughness

Detection of Removed Objects in 3D Meshes Using Up-to-Date Images for Mixed-Reality Applications

Moving Objects Aware Sensor Mesh Fusion for Indoor Reconstruction From a Couple of 2d Lidar Scans

Contact Info

Product

Resources

About