Factor Graph based 3D Multi-Object Tracking in Point Clouds (FG-3DMOT)

Batch3DMOT: 3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality AttentionПодробнее

GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature LearningПодробнее

Tracking of multiple 3d objects in point cloudsПодробнее

Demo video of "Structure-Aware Single Stage 3D Object Detection from Point Cloud", CVPR 2020Подробнее

3D object Detection and Tracking from raw Point cloudПодробнее

A Hierarchical Graph Network for 3D Object Detection on Point CloudsПодробнее

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point CloudsПодробнее

IROS 2020: 3D Multi-Object Tracking: A Baseline and New Evaluation MetricsПодробнее

3D Multi Object Tracking in Point Clouds Based on Prediction Confidence Guided Data AssociationПодробнее

Tracking of multiple 3d objects in point clouds(with partial occlusion)Подробнее

P2B: Point-to-Box Network for 3D Object Tracking in Point CloudsПодробнее

PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?Подробнее

3D Object Tracking in Point Clouds: Best Practices and New SettingsПодробнее

LiDAR object trackingПодробнее

Exploring Simple 3D Multi-Object Tracking for Autonomous DrivingПодробнее

Point-GNN: Graph Neural Network for 3D Object Detection in a Point CloudПодробнее

Model-free Vehicle Tracking and State Estimationin Point Cloud SequencesПодробнее

Multi-scale Graph Networks for 3D Point Cloud Segmentation (Chao Luo's PhD Thesis)Подробнее