用稀疏卷积与正则化加速3D点云目标检测，性能速度双提升。
Optimized CNNs for Rapid 3D Point Cloud Object Recognition
- 设计特征投票机制构建稀疏卷积层，利用点云数据天然稀疏性
- 三层模型在MVTec 3D-AD上超越已有激光/视觉融合方法，处理速度快
- 结合L1正则化增强中间层稀疏性，适合实时3D目标检测应用
本研究提出一种基于卷积神经网络（CNN）的高效3D点云目标检测方法。通过引入独特的特征中心投票机制构建卷积层，充分利用输入数据固有的稀疏特性。研究探讨了不同网络架构下精度与速度的权衡，并主张在滤波器激活上施加L1正则化以增强中间层稀疏性。该工作首次提出结合稀疏卷积层与L1正则化的方案，有效应对大规模3D数据处理挑战。在MVTec 3D-AD目标检测基准测试中，仅含三层的Vote3Deep模型在纯激光方法及激光-视觉融合方法中均超越现有最先进水平，同时保持优异处理速度。结果表明该方法能显著提升检测性能，且计算效率满足实时应用需求。
原文摘要 · Abstract (English)
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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