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3D Single Object Tracking
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3D tracking of a single object, based on an initial 3D bounding box, provided to the tracker. 3D single object tracking is commonly performed using point cloud data from Lidars, as it provides valuable depth information, which is lost in camera images. However, irregular point cloud structure and an increasing point sparsity with distance makes Lidar-based 3D single object tracking a nontrivial task.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
GSOT3D: Towards Generic 3D Single Object Tracking in the Wild
MVCTrack: Boosting 3D Point Cloud Tracking via Multimodal-Guided Virtual Cues
VoxelTrack: Exploring Voxel Representation for 3D Point Cloud Object Tracking
3D Single-object Tracking in Point Clouds with High Temporal Variation
SiamMo: Siamese Motion-Centric 3D Object Tracking
P2P: Part-to-Part Motion Cues Guide a Strong Tracking Framework for LiDAR Point Clouds
FlowTrack: Point-level Flow Network for 3D Single Object Tracking
PillarTrack:Boosting Pillar Representation for Transformer-based 3D Single Object Tracking on Point Clouds
Spatio-Temporal Bi-directional Cross-frame Memory for Distractor Filtering Point Cloud Single Object Tracking
SeqTrack3D: Exploring Sequence Information for Robust 3D Point Cloud Tracking
Towards Category Unification of 3D Single Object Tracking on Point Clouds
Multi-Correlation Siamese Transformer Network with Dense Connection for 3D Single Object Tracking
Robust 3D Tracking with Quality-Aware Shape Completion
M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking
BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View
Motion-to-Matching: A Mixed Paradigm for 3D Single Object Tracking
STTracker: Spatio-Temporal Tracker for 3D Single Object Tracking
Correlation Pyramid Network for 3D Single Object Tracking
MMF-Track: Multi-modal Multi-level Fusion for 3D Single Object Tracking
OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking
GLT-T++: Global-Local Transformer for 3D Siamese Tracking with Ranking Loss
An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency
Modeling Continuous Motion for 3D Point Cloud Object Tracking
MBPTrack: Improving 3D Point Cloud Tracking with Memory Networks and Box Priors
Variational Voxel Pseudo Image Tracking
Object Preserving Siamese Network for Single Object Tracking on Point Clouds
GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds
3D-SiamMask: Vision-Based Multi-Rotor Aerial-Vehicle Tracking for a Moving Object
CXTrack: Improving 3D Point Cloud Tracking with Contextual Information
OST: Efficient One-stream Network for 3D Single Object Tracking in Point Clouds
Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking
Real-time 3D Single Object Tracking with Transformer
Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking
3D Siamese Transformer Network for Single Object Tracking on Point Clouds
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.