{"ID":2854578,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.14576","arxiv_id":"2510.14576","title":"CALM-Net: Curvature-Aware LiDAR Point Cloud-based Multi-Branch Neural Network for Vehicle Re-Identification","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.","short_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 mult...","url_abs":"https://arxiv.org/abs/2510.14576","url_pdf":"https://arxiv.org/pdf/2510.14576v1","authors":"[\"Dongwook Lee\",\"Sol Han\",\"Jinwhan Kim\"]","published":"2025-10-16T11:36:54Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}