CXTrack: Improving 3D Point Cloud Tracking with Contextual Information
Author:
Affiliation:
1. Tsinghua University,China
2. Cardiff University,United Kingdom
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10203037/10203050/10205433.pdf?arnumber=10205433
Reference33 articles.
1. People tracking in RGB-D data with on-line boosted target models
2. Group-Free 3D Object Detection via Transformers
3. An End-to-End Transformer Model for 3D Object Detection
4. Voxel Transformer for 3D Object Detection
5. Box-aware feature en-hancement for single object tracking on point clouds;zheng;Proceedings of the IEEE/CVF International Conference on Computer Vision,0
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1. Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud;Applied Sciences;2024-09-05
2. RoMATer: An end-to-end robust multiaircraft tracker with transformer;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
3. CDTracker: Coarse-to-Fine Feature Matching and Point Densification for 3D Single-Object Tracking;Remote Sensing;2024-06-25
4. SeqTrack3D: Exploring Sequence Information for Robust 3D Point Cloud Tracking;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
5. Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
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