A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds-Reference-Cited by-同舟云学术

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

Published:2023-04-10 Issue:4 Volume:25 Page:635
ISSN:1099-4300
Container-title:Entropy
language:en
Short-container-title:Entropy

Author:

Vinodkumar Prasoon Kumar¹,Karabulut Dogus¹,Avots Egils¹,Ozcinar Cagri¹,Anbarjafari Gholamreza¹²³⁴

Affiliation:

1. iCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, Estonia

2. PwC Advisory, 00180 Helsinki, Finland

3. iVCV OÜ, 51011 Tartu, Estonia

4. Institute of Higher Education, Yildiz Technical University, Beşiktaş, Istanbul 34349, Turkey

Abstract

The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their outstanding performance in 2D computer vision. As a result, many innovative approaches have been proposed and validated on multiple benchmark datasets. This study offers an in-depth assessment of the latest developments in deep learning-based 3D object recognition. We discuss the most well-known 3D object recognition models, along with evaluations of their distinctive qualities.

Funder

European Union’s Horizon 2020 research and innovation program

Publisher

MDPI AG

Subject

General Physics and Astronomy

Link

https://www.mdpi.com/1099-4300/25/4/635/pdf

Reference90 articles.

1. Zhou, Y., and Tuzel, O. (2018, January 18–23). Voxelnet: End-to-end learning for point cloud based 3d object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.

2. Yan, Y., Mao, Y., and Li, B. (2018). SECOND: Sparsely Embedded Convolutional Detection. Sensors, 18.

3. Shi, S., Wang, X., and Li, H. (2019, January 15–20). PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA. Available online: http://xxx.lanl.gov/abs/1812.04244.

4. Yang, Z., Sun, Y., Liu, S., Shen, X., and Jia, J. (November, January 27). STD: Sparse-to-Dense 3D Object Detector for Point Cloud. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea. Available online: http://xxx.lanl.gov/abs/1907.10471.

5. Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., and Beijbom, O. (2018, January 18–23). PointPillars: Fast Encoders for Object Detection from Point Clouds. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA. Available online: http://xxx.lanl.gov/abs/1812.05784.

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