Point cloud up-sampling network with multi-level spatial local feature aggregation

2021 ◽  
Vol 94 ◽  
pp. 107337
Author(s):  
Guang Zeng ◽  
Haisheng Li ◽  
Xiaochuan Wang ◽  
Nan Li
Keyword(s):  
Point Cloud ◽  
Local Feature ◽  
Sampling Network ◽  
Feature Aggregation ◽  
Multi Level

scholarly journals Integrating Normal Vector Features into an Atrous Convolution Residual Network for LiDAR Point Cloud Classification

2021 ◽  
Vol 13 (17) ◽  
pp. 3427
Author(s):  
Chunjiao Zhang ◽  
Shenghua Xu ◽  
Tao Jiang ◽  
Jiping Liu ◽  
Zhengjun Liu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Spatial Information ◽  
Point Clouds ◽  
Local Feature ◽  
Normal Vector ◽  
Residual Network ◽  
Semantic Classification ◽  
Cloud Classification ◽  
Feature Aggregation ◽  
Point Cloud Classification

LiDAR point clouds are rich in spatial information and can effectively express the size, shape, position, and direction of objects; thus, they have the advantage of high spatial utilization. The point cloud focuses on describing the shape of the external surface of the object itself and will not store useless redundant information to describe the occupation. Therefore, point clouds have become the research focus of 3D data models and are widely used in large-scale scene reconstruction, virtual reality, digital elevation model production, and other fields. Since point clouds have various characteristics, such as disorder, density inconsistency, unstructuredness, and incomplete information, point cloud classification is still complex and challenging. To realize the semantic classification of LiDAR point clouds in complex scenarios, this paper proposes the integration of normal vector features into an atrous convolution residual network. Based on the RandLA-Net network structure, the proposed network integrates the atrous convolution into the residual module to extract global and local features of the point clouds. The atrous convolution can learn more valuable point cloud feature information by expanding the receptive field. Then, the point cloud normal vector is embedded in the local feature aggregation module of the RandLA-Net network to extract local semantic aggregation features. The improved local feature aggregation module can merge the deep features of the point cloud and mine the fine-grained information of the point cloud to improve the model’s segmentation ability in complex scenes. Finally, to resolve the imbalance of the distribution of the various categories of point clouds, the original loss function is optimized by adopting a reweighted method to prevent overfitting so that the network can focus on small target categories in the training process to effectively improve the classification performance. Through the experimental analysis of a Vaihingen (Germany) urban 3D semantic dataset from the ISPRS website, it is verified that the proposed algorithm has a strong generalization ability. The overall accuracy (OA) of the proposed algorithm on the Vaihingen urban 3D semantic dataset reached 97.9%, and the average reached 96.1%. Experiments show that the proposed algorithm fully exploits the semantic features of point clouds and effectively improves the accuracy of point cloud classification.

scholarly journals Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3493
Author(s):  
Gahyeon Lim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Complex Environments ◽  
Indoor Space ◽  
Multi Level ◽  
Level Building ◽  
Indoor Spaces ◽  
Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

Learning Local Descriptors with Multi-Level Feature Aggregation and Spatial Context Pyramid

2021 ◽  
Author(s):  
Pengpeng Liang ◽  
Haoxuanye Ji ◽  
Erkang Cheng ◽  
Yumei Chai ◽  
Liming Wang ◽  
...  
Keyword(s):  
Spatial Context ◽  
Local Descriptors ◽  
Feature Aggregation ◽  
Multi Level

Local feature aggregation algorithm based on graph convolutional network

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Hao Wang ◽  
Liyan Dong ◽  
Minghui Sun
Keyword(s):  
Local Feature ◽  
Convolutional Network ◽  
Feature Aggregation
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