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홈뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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  4. Optical Flow Estimation On Kitti 2012

Optical Flow Estimation On Kitti 2012

평가 지표

Average End-Point Error

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Average End-Point Error
Paper TitleRepository
MaskFlownet1.1MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask
PWC-Net + ft - axXiv1.5Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation
LiteFlowNet3-S1.3LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation
FDFlowNet-ft1.5FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network-
CroCo-Flow0.8CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
FastFlowNet-ft1.8FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation
MaskFlownet-S1.1MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask
IRR-PWC1.6Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation
LiteFlowNet-ft1.6LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
LiteFlowNet2-ft1.4A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization
LiteFlowNet31.3LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation
SelFlow1.5SelFlow: Self-Supervised Learning of Optical Flow
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