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Optimized CNNs for Rapid 3D Point Cloud Object Recognition
Research Article | Published: 08 December 2024
IECE Transactions on Internet of Things Volume 2, Issue 4, 2024: 83-94
Volume 2, Issue 4, IECE Transactions on Internet of Things
Volume 2, Issue 4, 2024
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Jinchao Chen
Jinchao Chen
Northwestern Polytechnical University, China
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IECE Transactions on Internet of Things, Volume 2, Issue 4, 2024: 83-94

Free to Read | Research Article | 08 December 2024
Optimized CNNs for Rapid 3D Point Cloud Object Recognition
by
Tianyi Lyu Tianyi Lyu 1
Dian Gu Dian Gu 2
Peiyuan Chen Peiyuan Chen 3
Yaoting Jiang Yaoting Jiang 4
Zhenhong Zhang Zhenhong Zhang 5
Huadong Pang Huadong Pang 6
Li Zhou Li Zhou 7
Yiping Dong Yiping Dong 8 *
1 College of Engineering, Northeastern University, Boston 02115, MA, United States
2 University of Pennsylvania, Philadelphia 19104, PA, United States
3 School of Electrical Engineering and Computer Science, Oregon State University, Corvallis 97333, OR, United States
4 Carnegie Mellon University, College of Engineering, Pittsburgh 15213, PA, United States
5 George Washington University, Washington 20052, DC, United States
6 Georgia Institute of Technology, Atlanta 30332, GA, United States
7 Faculty of Management, McGill University, Montreal H3B0C7, QC, Canada
8 Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh 15213, PA, United States
* Corresponding Author: Yiping Dong, [email protected]
DOI: 10.62762/TIOT.2024.758153
Received: 09 October 2024, Accepted: 19 November 2024, Published: 08 December 2024  
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Abstract
This study introduces a method for efficiently detecting objects within 3D point clouds using convolutional neural networks (CNNs). Our approach adopts a unique feature-centric voting mechanism to construct convolutional layers that capitalize on the typical sparsity observed in input data. We explore the trade-off between accuracy and speed across diverse network architectures and advocate for integrating an L1 penalty on filter activations to augment sparsity within intermediate layers. This research pioneers the proposal of sparse convolutional layers combined with L1 regularization to effectively handle large-scale 3D data processing. Our method’s efficacy is demonstrated on the MVTec 3D-AD object detection benchmark. The Vote3Deep models, with just three layers, outperform the previous state-of-the-art in both laser-only approaches and combined laser-vision methods. Additionally, they maintain competitive processing speeds. This underscores our approach’s capability to substantially enhance detection performance while ensuring computational efficiency suitable for real-time applications.
Graphical Abstract
Optimized CNNs for Rapid 3D Point Cloud Object Recognition
Keywords
object detection
L1 penalty
point cloud
MVTec 3D-AD
Funding
This work was supported without any funding.
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Cite This Article
APA Style
Lyu, T., Gu, D., Chen, P., Jiang, Y., Zhang, Z., Pang, H., Zhou, L., & Dong, Y. (2024). Optimized CNNs for Rapid 3D Point Cloud Object Recognition. IECE Transactions on Internet of Things, 2(4), 83–94. https://doi.org/10.62762/TIOT.2024.758153
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