scholarly journals Effective Point Cloud Analysis Using Multi-Scale Features

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5574
Author(s):  
Qiang Zheng ◽  
Jian Sun
Keyword(s):  
Spatial Distribution ◽  
Spatial Relationship ◽  
Point Clouds ◽  
Local Features ◽  
Multi Scale ◽  
Proposed Model ◽  
Local Patterns ◽  
Previous State ◽  
Lightweight Structure ◽  
Independent Features

Fully exploring the correlation of local features and their spatial distribution in point clouds is essential for feature modeling. This paper, inspired by convolutional neural networks (CNNs), explores the relationship between local patterns and point coordinates from a novel perspective and proposes a lightweight structure based on multi-scale features and a two-step fusion strategy. Specifically, local features of multi-scales and their spatial distribution can be regarded as independent features corresponding to different levels of geometric significance, which are extracted by multiple parallel branches and then merged on multiple levels. In this way, the proposed model generates a shape-level representation that contains rich local characteristics and the spatial relationship between them. Moreover, with the shared multi-layer perceptrons (MLPs) as basic operators, the proposed structure is so concise that it converges rapidly, and so we introduce the snapshot ensemble to improve performance further. The model is evaluated on classification and part segmentation tasks. The experiments prove that our model achieves on-par or better performance than previous state-of-the-art (SOTA) methods.

scholarly journals Recognizing Objects in 3D Point Clouds with Multi-Scale Local Features

Sensors ◽  
2014 ◽  
Vol 14 (12) ◽  
pp. 24156-24173 ◽  
Author(s):  
Min Lu ◽  
Yulan Guo ◽  
Jun Zhang ◽  
Yanxin Ma ◽  
Yinjie Lei
Keyword(s):  
Point Clouds ◽  
Local Features ◽  
Multi Scale ◽  
3D Point Clouds

Pairwise registration of TLS point clouds by deep multi-scale local features

2020 ◽  
Vol 386 ◽  
pp. 232-243
Author(s):  
Wei Li ◽  
Cheng Wang ◽  
Chenglu Wen ◽  
Zheng Zhang ◽  
Congren Lin ◽  
...  
Keyword(s):  
Point Clouds ◽  
Local Features ◽  
Multi Scale ◽  
Pairwise Registration

A Fuzzy Spatial Relationship Graph for Point Clouds Using Bounding Boxes

2021 ◽  
Author(s):  
Andrew R. Buck ◽  
Derek T. Anderson ◽  
James M. Keller ◽  
Robert H. Luke ◽  
Grant Scott
Keyword(s):  
Spatial Relationship ◽  
Point Clouds ◽  
Bounding Boxes

scholarly journals Aggregation-and-Attention Network for brain tumor segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chih-Wei Lin ◽  
Yu Hong ◽  
Jinfu Liu
Keyword(s):  
Brain Tumor ◽  
Semantic Information ◽  
Spatial Relationship ◽  
Tumor Segmentation ◽  
Computer Assisted ◽  
Brain Tumor Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Assisted Diagnosis ◽  
The Brain

Abstract Background Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation.

scholarly journals An Inverse Node Graph-Based Method for the Urban Scene Segmentation of 3D Point Clouds

2021 ◽  
Vol 13 (15) ◽  
pp. 3021
Author(s):  
Bufan Zhao ◽  
Xianghong Hua ◽  
Kegen Yu ◽  
Xiaoxing He ◽  
Weixing Xue ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Point Clouds ◽  
Intelligent Vehicles ◽  
Critical Data ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Cluster Optimization ◽  
Urban Scene ◽  
Processing Steps

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.

scholarly journals Multi-View Laser Point Cloud Global Registration for a Single Object

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3729 ◽  
Author(s):  
Shuai Wang ◽  
Hua-Yan Sun ◽  
Hui-Chao Guo ◽  
Lin Du ◽  
Tian-Jian Liu
Keyword(s):  
Spatial Distribution ◽  
Objective Function ◽  
Point Cloud ◽  
Point Clouds ◽  
Low Rank ◽  
Single Object ◽  
Loop Closure Detection ◽  
Global Registration ◽  
Distribution Features ◽  
Better Than

Global registration is an important step in the three-dimensional reconstruction of multi-view laser point clouds for moving objects, but the severe noise, density variation, and overlap ratio between multi-view laser point clouds present significant challenges to global registration. In this paper, a multi-view laser point cloud global registration method based on low-rank sparse decomposition is proposed. Firstly, the spatial distribution features of point clouds were extracted by spatial rasterization to realize loop-closure detection, and the corresponding weight matrix was established according to the similarities of spatial distribution features. The accuracy of adjacent registration transformation was evaluated, and the robustness of low-rank sparse matrix decomposition was enhanced. Then, the objective function that satisfies the global optimization condition was constructed, which prevented the solution space compression generated by the column-orthogonal hypothesis of the matrix. The objective function was solved by the Augmented Lagrange method, and the iterative termination condition was designed according to the prior conditions of single-object global registration. The simulation analysis shows that the proposed method was robust with a wide range of parameters, and the accuracy of loop-closure detection was over 90%. When the pairwise registration error was below 0.1 rad, the proposed method performed better than the three compared methods, and the global registration accuracy was better than 0.05 rad. Finally, the global registration results of real point cloud experiments further proved the validity and stability of the proposed method.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

Framework for automated registration of UAV and UGV point clouds using local features in images

2019 ◽  
Vol 98 ◽  
pp. 175-182 ◽  
Author(s):  
Jisoo Park ◽  
Pileun Kim ◽  
Yong K. Cho ◽  
Junsuk Kang
Keyword(s):  
Point Clouds ◽  
Local Features ◽  
Automated Registration

scholarly journals A nonlinear multi-scale model for blood circulation in a realistic vascular system

2021 ◽  
Vol 8 (12) ◽  
Author(s):  
Ulin Nuha A. Qohar ◽  
Antonella Zanna Munthe-Kaas ◽  
Jan Martin Nordbotten ◽  
Erik Andreas Hanson
Keyword(s):  
Blood Flow ◽  
Blood Circulation ◽  
Vascular System ◽  
Scale Model ◽  
Model Framework ◽  
Fluid Exchange ◽  
Multi Scale ◽  
Proposed Model ◽  
Porous Media Model ◽  
Capillary Bed

In the last decade, numerical models have become an increasingly important tool in biological and medical science. Numerical simulations contribute to a deeper understanding of physiology and are a powerful tool for better diagnostics and treatment. In this paper, a nonlinear multi-scale model framework is developed for blood flow distribution in the full vascular system of an organ. We couple a quasi one-dimensional vascular graph model to represent blood flow in larger vessels and a porous media model to describe flow in smaller vessels and capillary bed. The vascular model is based on Poiseuille’s Law, with pressure correction by elasticity and pressure drop estimation at vessels' junctions. The porous capillary bed is modelled as a two-compartment domain (artery and venous) using Darcy’s Law. The fluid exchange between the artery and venous capillary bed compartments is defined as blood perfusion. The numerical experiments show that the proposed model for blood circulation: (i) is closely dependent on the structure and parameters of both the larger vessels and of the capillary bed, and (ii) provides a realistic blood circulation in the organ. The advantage of the proposed model is that it is complex enough to reliably capture the main underlying physiological function, yet highly flexible as it offers the possibility of incorporating various local effects. Furthermore, the numerical implementation of the model is straightforward and allows for simulations on a regular desktop computer.

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