CALM-Net: Curvature-Aware LiDAR Point Cloud-based Multi-Branch Neural Network for Vehicle Re-Identification
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Vehicle Re-IdentificationPoint CloudsSimilar Papers 제목 키워드 기반
Pseudo-LiDAR for Visual Odometry
In the existing methods, LiDAR odometry shows superior performance, but visual odometry is still widely used for its price advantage. Conventionally, the task of visual odometry mainly rely on the input of continuous ima…
Stereo MatchingVisual OdometryLidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition
Point clouds have gained growing interest in gait recognition. However, current methods, which typically convert point clouds into 3D voxels, often fail to extract essential gait-specific features. In this paper, we…
Gait RecognitionRepresentation LearningEstimating Discrete Total Curvature with Per Triangle Normal Variation
We introduce a novel approach for measuring the total curvature at every triangle of a discrete surface. This method takes advantage of the relationship between per triangle total curvature and the Dirichlet energy of th…
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds
3D perception in LiDAR point clouds is crucial for a self-driving vehicle to properly act in 3D environment. However, manually labeling point clouds is hard and costly. There has been a growing interest in self-supervise…
3D Object Detection3D Semantic SegmentationAutonomous DrivingContrastive Learning+4RasterNet: Modeling Free-Flow Speed using LiDAR and Overhead Imagery
Roadway free-flow speed captures the typical vehicle speed in low traffic conditions. Modeling free-flow speed is an important problem in transportation engineering with applications to a variety of design, operation, pl…