Ground Segmentation From Large-Scale Terrestrial Laser Scanner Data of Industrial Environments

Giorgini, Mikhail; Barbieri, Filippo; Aleotti, Jacopo

doi:10.1109/lra.2017.2715378

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Despite their applicability in a wide range of applications such as denoising, compression and watermarking, they require the computation of explicit eigenvector making them prohibitive for real-time scenarios. Additionally, there are a lot of applications in literature in which large-scale 3D models are scanned in parts [ 23 , 24 , 25 ] providing in this way a consecutive sequence of coherent 3D surfaces that need to be processed fast. Our method has been designed in order to be ideally suited particularly in these cases, providing accurate results while the whole process takes part in real-time.…”

Section: Previous Workmentioning

confidence: 99%

Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations

2020

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Recently, spectral methods have been extensively used in the processing of 3D meshes. They usually take advantage of some unique properties that the eigenvalues and the eigenvectors of the decomposed Laplacian matrix have. However, despite their superior behavior and performance, they suffer from computational complexity, especially while the number of vertices of the model increases. In this work, we suggest the use of a fast and efficient spectral processing approach applied to dense static and dynamic 3D meshes, which can be ideally suited for real-time denoising and compression applications. To increase the computational efficiency of the method, we exploit potential spectral coherence between adjacent parts of a mesh and then we apply an orthogonal iteration approach for the tracking of the graph Laplacian eigenspaces. Additionally, we present a dynamic version that automatically identifies the optimal subspace size that satisfies a given reconstruction quality threshold. In this way, we overcome the problem of the perceptual distortions, due to the fixed number of subspace sizes that is used for all the separated parts individually. Extensive simulations carried out using different 3D models in different use cases (i.e., compression and denoising), showed that the proposed approach is very fast, especially in comparison with the SVD based spectral processing approaches, while at the same time the quality of the reconstructed models is of similar or even better reconstruction quality. The experimental analysis also showed that the proposed approach could also be used by other denoising methods as a preprocessing step, in order to optimize the reconstruction quality of their results and decrease their computational complexity since they need fewer iterations to converge.

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Section: Previous Workmentioning

confidence: 99%

Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations

2020

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“…In this paper, preprocessing mainly consists of two processes: ground segmentation and grid filtering. In general, ground modeling is utilized to segment point clouds into ground points and nonground points [19,[21][22][23]. Grid filtering [20] filters outliers and tall structural objects that are obviously nonhuman beings.…”

Section: Preprocessingmentioning

confidence: 99%

Pedestrian Detection with Lidar Point Clouds Based on Single Template Matching

Liu

Wang

2019

Electronics

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In the field of intelligent transportation systems, pedestrian detection has become a problem that is urgently in need of a solution. Effective pedestrian detection reduces accidents and protects pedestrians from injuries. A pedestrian-detection algorithm, namely, single template matching with kernel density estimation clustering (STM-KDE), is proposed in this paper. First, the KDE-based clustering method is utilized to extract candidate pedestrians in point clouds. Next, the coordinates of the point clouds are transformed into the pedestrians’ local coordinate system and projection images are generated. Locally adaptive regression kernel features are extracted from the projection image and matched with the template features by using cosine similarity, based on which pedestrians are distinguished from other columnar objects. Finally, comparative experiments using KITTI datasets are conducted to verify pedestrian-detection performance. Compared with the STM with radially bounded nearest neighbor (STM-RBNN) algorithm and the KDE-based pedestrian-detection algorithm, the proposed algorithm can segment gathering pedestrians and distinguish them from other columnar objects in real scenarios.

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“…Despite their applicability in a wide range of applications such as mesh denoising, geometry compression and watermarking, they require explicit eigenvector computations making them prohibitive for real time scenarios. Additionally, there are a lot of applications in literature in which large-scale 3D models are scanned in parts [16] , [17] , [18] providing a sequence of 3D surfaces that need to be processed fast and sequentially in time. Our method has been designed in order to be ideally suited particularly in these cases, providing accurate results while the whole process takes part in real-time.…”

Section: Related Workmentioning

confidence: 99%

Block-Based Spectral Processing of Static and Dynamic 3D Meshes using Orthogonal Iterations

Arvanitis¹,

Lalos²,

Moustakas³

2018

Preprint

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Spectral methods are widely used in geometry processing of 3D models. They rely on the projection of the mesh geometry on the basis defined by the eigenvectors of the graph Laplacian operator, becoming computationally prohibitive as the density of the models increases. In this paper, we propose a novel approach for supporting fast and efficient spectral processing of dense 3D meshes, ideally suited for real-time compression and denoising scenarios. To achieve that, we apply the problem of tracking graph Laplacian eigenspaces via orthogonal iterations, exploiting potential spectral coherence between adjacent parts. To avoid perceptual distortions when a fixed number of eigenvectors is used for all the individual parts, we propose a flexible solution that automatically identifies the optimal subspace size for satisfying a given reconstruction quality constraint. Extensive simulations carried out with different 3D meshes in compression and denoising setups, showed that the proposed schemes are very fast alternatives of SVD based spectral processing while achieving at the same time similar or even better reconstruction quality. More importantly, the proposed approach can be employed by several other state-of-the-art denoising methods as a preprocessing step, optimizing both their reconstruction quality and their computational complexity.

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Ground Segmentation From Large-Scale Terrestrial Laser Scanner Data of Industrial Environments

Cited by 8 publications

References 22 publications

Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations

Spectral Processing for Denoising and Compression of 3D Meshes Using Dynamic Orthogonal Iterations

Pedestrian Detection with Lidar Point Clouds Based on Single Template Matching

Block-Based Spectral Processing of Static and Dynamic 3D Meshes using Orthogonal Iterations

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