LatticeNet: fast spatio-temporal point cloud segmentation using permutohedral lattices

Roşu, Radu Alexandru; Schütt, Peer; Quenzel, Jan; Behnke, Sven

doi:10.1007/s10514-021-09998-1

Cited by 29 publications

(8 citation statements)

References 41 publications

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“…Based on these findings, MinkowskiNet [134] enables the direct processing of 3D sequences (e.g., LiDAR streams) with 4D spatiotemporal ConvNets with generalized sparse convolutions. Contrary to cubic discretization, approaches like LatticeNet [135] tessellate the scene space with d-dimensional permutohedral lattices into d-dimensional simplices (simplices are triangles for d = 2 and tetrahedrons for d = 3). The vertices of the permutohedral lattice store only the simplices, which contain non-empty regions.…”

Section: Discretization-based Methodsmentioning

confidence: 99%

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

Rauch

Braml

2023

Applied Sciences

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Point cloud learning has recently gained strong attention due to its applications in various fields, like computer vision, robotics, and autonomous driving. Point cloud semantic segmentation (PCSS) enables the automatic extraction of semantic information from 3D point cloud data, which makes it a desirable task for construction-related applications as well. Yet, only a limited number of publications have applied deep-learning-based methods to address point cloud understanding for civil engineering problems, and there is still a lack of comprehensive reviews and evaluations of PCSS methods tailored to such use cases. This paper aims to address this gap by providing a survey of recent advances in deep-learning-based PCSS methods and relating them to the challenges of the construction industry. We introduce its significance for the industry and provide a comprehensive look-up table of publicly available datasets for point cloud understanding, with evaluations based on data scene type, sensors, and point features. We address the problem of class imbalance in 3D data for machine learning, provide a compendium of commonly used evaluation metrics for PCSS, and summarize the most significant deep learning methods developed for PCSS. Finally, we discuss the advantages and disadvantages of the methods for specific industry challenges. Our contribution, to the best of our knowledge, is the first survey paper that comprehensively covers deep-learning-based methods for semantic segmentation tasks tailored to construction applications. This paper serves as a useful reference for prospective research and practitioners seeking to develop more accurate and efficient PCSS methods.

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Section: Discretization-based Methodsmentioning

confidence: 99%

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

Rauch

Braml

2023

Applied Sciences

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“…Most successful approaches use voxelized [23], [25], bird's eye view [26], or spherical projection input [27], [28], [29], [30]. However, raw point input approaches exist as well [31], [32], [33], [34], [35], [36], [37], [38] Cylinder3D [23] parts the point cloud into cylindrical voxels. Then a 3D convolutional neural network extracts features and produces the predictions.…”

Section: B Learned Approachesmentioning

confidence: 99%

4DenoiseNet: Adverse Weather Denoising From Adjacent Point Clouds

Seppänen

Ojala

Tammi

2023

IEEE Robot. Autom. Lett.

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Reliable point cloud data is essential for perception tasks e.g. in robotics and autonomous driving applications. Adverse weather causes a specific type of noise to light detection and ranging (LiDAR) sensor data, which degrades the quality of the point clouds significantly. To address this issue, this letter presents a novel point cloud adverse weather denoising deep learning algorithm (4DenoiseNet). Our algorithm takes advantage of the time dimension unlike deep learning adverse weather denoising methods in the literature. It performs about 10% better in terms of intersection over union metric compared to the previous work and is more computationally efficient. These results are achieved on our novel SnowyKITTI dataset, which has over 40000 adverse weather annotated point clouds. Moreover, strong qualitative results on the Canadian Adverse Driving Conditions dataset indicate good generalizability to domain shifts and to different sensor intrinsics.

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“…Current approaches can be divided into three categories, based on the input representations: projection-based, voxel-based, and pointbased. The projection-based methods, including [14,19,25,30,33,34], leverage well-established 2D convolutional networks by projecting 3D point clouds into 2D images. SqueezeSeg [33,34] projects a 3D LiDAR point cloud onto a spherical surface.…”

Section: Related Workmentioning

confidence: 99%

PointMotionNet: Point-Wise Motion Learning for Large-Scale LiDAR Point Clouds Sequences

Wang

Sullivan

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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We propose a point-based spatiotemporal pyramid architecture, called PointMotionNet, to learn motion information from a sequence of large-scale 3D LiDAR point clouds. A core component of PointMotionNet is a novel technique for point-based spatiotemporal convolution, which finds the point correspondences across time by leveraging a timeinvariant spatial neighboring space and extracts spatiotemporal features. To validate PointMotionNet, we consider two motion-related tasks: point-based motion prediction and multisweep semantic segmentation. For each task, we design an end-to-end system where PointMotionNet is the core module that learns motion information. We conduct extensive experiments and show that i) for pointbased motion prediction, PointMotionNet achieves less than 0.5m mean squared error on Argoverse dataset, which is a significant improvement over existing methods; and ii) for multisweep semantic segmentation, PointMotionNet with a pretrained segmentation backbone outperforms previous SOTA by over 3.3 % mIoU on SemanticKITTI dataset with 25 classes including 6 moving objects.

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LatticeNet: fast spatio-temporal point cloud segmentation using permutohedral lattices

Cited by 29 publications

References 41 publications

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

4DenoiseNet: Adverse Weather Denoising From Adjacent Point Clouds

PointMotionNet: Point-Wise Motion Learning for Large-Scale LiDAR Point Clouds Sequences

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