Search Results for author:
Found 7 papers, 5 papers with code
Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking
Multiple object tracking in complex scenarios - such as coordinated dance performances, team sports, or dynamic animal groups - presents unique challenges.
SLAck: Semantic, Location, and Appearance Aware Open-Vocabulary Tracking
Due to the complexity of motion patterns in the large-vocabulary scenarios and unstable classification of the novel objects, the motion and semantics cues are either ignored or applied based on heuristics in the final matching steps by existing methods.
UniDepth: Universal Monocular Metric Depth Estimation
However, the remarkable accuracy of recent MMDE methods is confined to their training domains.
Ranked #5 on
Monocular Depth Estimation
on KITTI Eigen split
(using extra training data)
CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking
To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential.
Ranked #4 on
3D Multi-Object Tracking
on nuscenes Camera-Radar
CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion
To track the 3D locations and trajectories of the other traffic participants at any given time, modern autonomous vehicles are equipped with multiple cameras that cover the vehicle's full surroundings.
Dense Prediction with Attentive Feature Aggregation
Our experiments show consistent and significant improvements on challenging semantic segmentation benchmarks, including Cityscapes, BDD100K, and Mapillary Vistas, at negligible computational and parameter overhead.
Monocular Quasi-Dense 3D Object Tracking
Experiments on our proposed simulation data and real-world benchmarks, including KITTI, nuScenes, and Waymo datasets, show that our tracking framework offers robust object association and tracking on urban-driving scenarios.
Ranked #15 on
Multiple Object Tracking
on KITTI Test (Online Methods)
