Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Sep 27;22(19):7346.
doi: 10.3390/s22197346.

Point Cloud Completion Network Applied to Vehicle Data

Xuehan Ma  1 , Xueyan Li  1 , Junfeng Song  1   2
Affiliations

Point Cloud Completion Network Applied to Vehicle Data

Xuehan Ma et al. Sensors (Basel). .

Abstract

With the development of autonomous driving, augmented reality, and other fields, it is becoming increasingly important for machines to more accurately and comprehensively perceive their surrounding environment. LiDAR is one of the most important tools used by machines to obtain information about the surrounding environment. However, because of occlusion, the point cloud data obtained by LiDAR are not the complete shape of the object, and completing the incomplete point cloud shape is of great significance for further data analysis, such as classification and segmentation. In this study, we examined the completion of a 3D point cloud and improved upon the FoldingNet auto-encoder. Specifically, we used the encoder-decoder architecture to design our point cloud completion network. The encoder part uses the transformer module to enhance point cloud feature extraction, and the decoder part changes the 2D lattice used by the A network into a 3D lattice so that the network can better fit the shape of the 3D point cloud. We conducted experiments on point cloud datasets sampled from the ShapeNet car-category CAD models to verify the effectiveness of the various improvements made to the network.

Keywords: neural networks; point clouds; transformer.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Network architecture mainly consists of a transformer-based encoder and an improved fold-based decoder.
Figure 2
Figure 2
Network architecture of the transformer encoder comprises (a) point transformer layer, (b) point transformer block, and (c) transition down block.
Figure 3
Figure 3
Details of data generation.
Figure 4
Figure 4
Output results of different methods. Occluded part represents the predicted point cloud of the occluded part, complete represents the complete point cloud after splicing with the unoccluded part. Since FoldingNet (1) was trained using the first method, which directly outputs the completion point cloud, there is no occluded part column.
Figure 5
Figure 5
Comparison of using 3D versus 2D lattice.
Figure 6
Figure 6
Some point cloud shapes with poor completion.
Figure 7
Figure 7
The completion shape of different densities.
Figure 8
Figure 8
The completion shape of different occlusion percentages.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Hegde V., Zadeh R. FusionNet: 3D Object Classification Using Multiple Data Representations. arXiv. 20161607.05695
    1. Qi C.R., Su H., Mo K., Guibas L.J. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation; Proceedings of the 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); Honolulu, HI, USA. 21–26 July 2017.
    1. Wang Y., Tan D.J., Navab N., Tombari F. SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification; Proceedings of the 16th European Conference; Glasgow, UK. 23–28 August 2020.
    1. Qi C.R., Yi L., Su H., Guibas L.J. PointNet plus plus: Deep Hierarchical Feature Learning on Point Sets in a Metric Space; Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS); Long Beach, CA, USA. 4–9 December 2017.
    1. Atik M.E., Duran Z. An Efficient Ensemble Deep Learning Approach for Semantic Point Cloud Segmentation Based on 3D Geometric Features and Range Images. Sensors. 2022;22:6210. doi: 10.3390/s22166210. - DOI - PMC - PubMed

LinkOut - more resources