Research of Deep Learning-Based Semantic Segmentation for 3D Point Cloud

被引：5

作者：

Wang, Tao ^{[1
]}

Wang, Wenju ^{[1
]}

Cai, Yu ^{[1
]}

机构：

[1] University of Shanghai for Science and Technology, Shanghai

来源：

Computer Engineering and Applications | 2024年 / 57卷 / 23期

关键词：

3D point cloud; computer vision; deep learning; intelligent packaging; semantic segmentation;

D O I：

10.3778/j.issn.1002-8331.2107-0142

中图分类号：

学科分类号：

摘要：

This paper summarizes the methods of deep learning-based semantic segmentation for 3D point cloud. The literature research method is used to describe deep learning-based semantic segmentation methods for 3D point cloud according to the representation of data. It discusses the current situation of domestic and foreign development in recent years, and analyzes the advantages and disadvantages of the current related methods, and prospects the future development trend. Deep learning plays an extremely important role in the research of semantic segmentation technology for point cloud, and promotes the manufacturing, packaging fields and etc to development in the direction of intelligence. According to the advantages and disadvantages of various methods, it is an important research direction to construct a framework model of semantic segmentation combined with 2D-3D for projection, voxel, multi-view and point cloud in the future. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：18 / 26

页数：8

共 62 条

[31]

TCHAPMI L, CHOY C, ARMENI I, Et al., SEGCloud：semantic segmentation of 3d point clouds[C], 2017 International Conference on 3D Vision（3DV）, pp. 537-547, (2017)

[32]

DAI A, RITCHIE D., ScanComplete：large-scale scene completion and semantic segmentation for 3d scans[C], Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4578-4587, (2018)

[33]

LONG J, SHELHAMER E, DARRELL T., Fully convolutional networks for semantic segmentation[C], Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440, (2015)

[34]

LE T, DUAN Y., PointGrid：a deep network for 3d shape understanding[C], Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9204-9214, (2018)

[35]

MENG H Y, GAO L, LAI Y K, Et al., Vv-net：voxel vae net with group convolutions for point cloud segmentation[C], Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8500-8508, (2019)

[36]

SU H, Et al., Multi-view convolutional neural networks for 3d shape recognition[C], Proceedings of the IEEE International Conference on Computer Vision, pp. 945-953, (2015)

[37]

JIANG J, BAO D, CHEN Z, Et al., MLVCNN：multi-loop-view convolutional neural network for 3D shape retrieval[C], Proceedings of the AAAI Conference on Artificial Intelligence, pp. 8513-8520, (2019)

[38]

TATARCHENKO M, PARK J, KOLTUN V, Et al., Tangent convolutions for dense prediction in 3d[C], Pro-ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3887-3896, (2018)

[39]

YU T, MENG J, YUAN J., Multi-view harmonized bilinear network for 3d object recognition[C], Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 186-194, (2018)

[40]

LI J, ZHANG X, LI J, Et al., Building and optimization of 3D semantic map based on LiDAR and camera fusion[J], Neurocomputing, 409, pp. 394-407, (2020)

← 1 2 3 4 5 6 7 →