Latest Research in Computer Vision and Pattern Recognition

Top Research Topics in Computer Vision and Pattern Recognition

• Computer Vision and Pattern Recognition — 14 papers
• 3D Shape Modeling and Analysis — 5 papers
• Remote Sensing and LiDAR Applications — 4 papers
• Robotics — 3 papers
• Advanced Neural Network Applications — 3 papers
• Manufacturing Process and Optimization — 2 papers
• Mineral Processing and Grinding — 1 papers
• Robotics and Sensor-Based Localization — 1 papers
• Target Tracking and Data Fusion in Sensor Networks — 1 papers
• Underwater Acoustics Research — 1 papers

Highest-Cited Papers

1. TOTMSeg: A Texture-Aware Octree-Based Transformer-Mamba Framework for Large-Scale Urban Mesh Semantic Segmentation
2. Instance Segmentation and Fine-grained Classification for Urban Buildings with Adaptive Region Dividing and Spatially-Supervised Contrastive Learning
3. LiteViLNet: Lightweight Vision-LiDAR Fusion Network for Efficient Road Segmentation
4. RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation
5. RGB-D Semantic-Guided 3D Geometric Estimation for Intelligent Coal Gangue Separation
6. SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completion
7. Unsupervised Point Cloud Registration via Training-Time Semantic Guidance
8. Learning Deformation-Induced Shape Representations for Building Footprints via Self-Supervision
9. LiPS: Lightweight Panoptic Segmentation for Resource-Constrained Robotics
10. Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds
11. SDA-Reg: large-scale dynamic scene point cloud registration with semantic dual-stream attention
12. WeatherMamba: Reliable Geometry-Aware Generalization for Adverse-Weather LiDAR Sensor Point-Cloud Semantic Segmentation
13. MSSP: Multi-Scale Spatially-Constrained Partition for Unsupervised Semantic Segmentation of 3D Point Clouds
14. DAGLFNet: Deep Feature Attention Guided Global and Local Feature Fusion for Pseudo-Image Point Cloud Segmentation
15. FeaturePointNet: A Semi-Supervised Geometric-Aware Graph Transformer for Dominant Point Extraction from Building Outlines
16. A Framework for Interactive 3D Segmentation with Adaptive Masking and Fine-Grained Version Control
17. GSSP-KAN: An Efficient Kansformer-Based Network with Grouped Separable Sparse Convolution for Large-Scale LiDAR Point Cloud Semantic Segmentation
18. RoadMark-AWAConv: Adaptive Weight-Anchor Convolution for Fine-Grained Semantic Segmentation of Road Marking Point Clouds
19. DS-RangeNet: Lightweight Dual-Stream LiDAR Semantic Segmentation for Industrial Indoor Environments
20. Semantic Segmentation for 3D Point Clouds with Curvature-Aware Sampling and Inverse-Density Weighting