Optimized CNNs for Rapid 3D Point Cloud Object Recognition

• URL: http://arxiv.org/abs/2412.02855v1
• Date: Tue, 03 Dec 2024 21:42:30 GMT
• Title: Optimized CNNs for Rapid 3D Point Cloud Object Recognition
• Authors: Tianyi Lyu, Dian Gu, Peiyuan Chen, Yaoting Jiang, Zhenhong Zhang, Huadong Pang, Li Zhou, Yiping Dong,
• Abstract summary: 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. The Vote3Deep models, with just three layers, outperform the previous state-of-the-art in both laser-only approaches and combined laser-vision methods.
• Score: 2.6462438855724826
• License:
• 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 $\mathcal{L}_1$ penalty on filter activations to augment sparsity within intermediate layers. This research pioneers the proposal of sparse convolutional layers combined with $\mathcal{L}_1$ 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.

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