利用曲率信息提升激光雷达车辆重识别准确率
CALM-Net: Curvature-Aware LiDAR Point Cloud-based Multi-Branch Neural Network for Vehicle Re-Identification
- 多分支结构融合边缘卷积、点注意力与曲率嵌入
- 在nuScenes数据集上提升1.97%重识别准确率
- 适合激光雷达场景下的车辆身份识别研究
本文提出CALM-Net，一种基于激光雷达点云的多分支神经网络，用于车辆重识别。该模型通过多分支架构，结合边缘卷积、点注意力机制和曲率嵌入，捕捉点云中局部表面变化特征，学习更丰富的几何与上下文信息。实验在大规模nuScenes数据集上表明，相比研究中的最强基线，CALM-Net实现约1.97%的平均重识别准确率提升。结果验证了将曲率信息融入深度网络的有效性，并凸显了多分支特征学习在激光雷达车辆重识别任务中的优势。
原文摘要 · Abstract (English)
This paper presents CALM-Net, a curvature-aware LiDAR point cloud-based multi-branch neural network for vehicle re-identification. The proposed model addresses the challenge of learning discriminative and complementary features from three-dimensional point clouds to distinguish between vehicles. CALM-Net employs a multi-branch architecture that integrates edge convolution, point attention, and a curvature embedding that characterizes local surface variation in point clouds. By combining these mechanisms, the model learns richer geometric and contextual features that are well suited for the re-identification task. Experimental evaluation on the large-scale nuScenes dataset demonstrates that CALM-Net achieves a mean re-identification accuracy improvement of approximately 1.97\% points compared with the strongest baseline in our study. The results confirms the effectiveness of incorporating curvature information into deep learning architectures and highlight the benefit of multi-branch feature learning for LiDAR point cloud-based vehicle re-identification.
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