Abstract
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.
Abstract (translated)
本文介绍了CALM-Net，这是一种基于曲率感知的LiDAR点云的多分支神经网络，用于车辆再识别。所提出的模型旨在解决从三维点云中学习区分性和互补性特征以区分不同车辆的挑战。CALM-Net采用了一种多分支架构，集成了边缘卷积、点注意力和一种能够表征点云局部表面变化的曲率嵌入。通过结合这些机制，该模型可以学习到更丰富的几何和上下文特征，非常适合于再识别任务。在大规模nuScenes数据集上的实验评估表明，CALM-Net相较于我们在研究中选取的最佳基线模型，在车辆再识别精度上提升了约1.97个百分点的平均值。结果证实了将曲率信息融入深度学习架构的有效性，并突出了多分支特征学习在基于LiDAR点云的车辆再识别中的优势。
URL
https://arxiv.org/abs/2510.14576
