Papers with Code 아카이브
Motion Forecasting
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Motion forecasting is the task of predicting the location of a tracked object in the future
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nvlabs/adaptive-prediction
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
https://github.com/nvlabs/catk
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
https://github.com/nvr-avg/adaptive-prediction
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
https://github.com/nvr-avg/trajdata
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
https://github.com/pagand/e2etransfuser
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
https://github.com/qiyan98/social-nce-stgcnn
Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/quan-dao/practical-collab-perception
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences
https://github.com/rohanchandra30/TrackNPred
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/romi514/TLSGAN
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/sancarlim/densetnt-intent
DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets
https://github.com/seu-zxj/llm-augmented-mtr
Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving
https://github.com/sshirahmad/GCRL
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
https://github.com/stepankonev/waymo-motion-prediction-chalenge-2022-multipath-plus-plus
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction
https://github.com/stepankonev/waymo-motion-prediction-challenge-2022-multipath-plus-plus
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction
https://github.com/taco-group/stamp
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
https://github.com/vita-epfl/s-attack
Are socially-aware trajectory prediction models really socially-aware?
https://github.com/vita-epfl/semdisc
Are socially-aware trajectory prediction models really socially-aware?
https://github.com/vita-epfl/social-nce-crowdnav
Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/vita-epfl/unitraj
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
https://github.com/wenkaip-1836890/pyromid_l5prediction
One Thousand and One Hours: Self-driving Motion Prediction Dataset
https://github.com/westny/mtp-go
Toward Unified Practices in Trajectory Prediction Research on Bird's-Eye-View Datasets
https://github.com/xiaogangpeng/socialtgcn
Trajectory-Aware Body Interaction Transformer for Multi-Person Pose Forecasting
https://github.com/zhanwei-z/g2ltraj
https://github.com/zhejz/hptr
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
https://github.com/zhixinlai/llmboostmedical
Frozen Transformers in Language Models Are Effective Visual Encoder Layers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.