Optical Flow on MVSEC 1.0 (indoor_flying2)
0.38EPEScaleEvent
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| ScaleEventBackbone=ViT-S/16, Protocol=Event Pretraining + Fully-Supervised2026.03 | 0.38 | 0.001 | |
| STPBackbone=Swin-T/7, Protocol=Event Pretraining + Fully-Supervised2026.03 | 0.41 | 0.001 | |
| ECDDPBackbone=Swin-T/7, Protocol=Event Pretraining + Fully-Supervised2026.03 | 0.45 | 0.002 | |
| EDCFlow (+in3)dt=1, Input=E, Learning paradigm=SL, training_inclusion=flying32025.06 | 0.54 | 0.34 | |
| MultiCM (Burgers')dt=1, Input=E, Learning paradigm=MB2025.06 | 0.6 | 0.59 | |
| EDCFlow (+in1)dt=1, Input=E, Learning paradigm=SL, training_inclusion=flying12025.06 | 0.65 | 1.85 | |
| ECDDPBackbone=ViT-S/16, Protocol=Event Pretraining + Fully-Supervised2026.03 | 0.69 | 0.29 | |
| EV-MGRFlowNetdt=1, Input=E, Learning paradigm=USL2025.06 | 0.7 | 2.35 | |
| STE-FlowNetdt=1, Input=E, Learning paradigm=SSL2025.06 | 0.79 | 1.6 | |
| DCEIFlowdt=1, Input=E+I1, Learning paradigm=SL2025.06 | 0.9 | 2.1 | |
| Brebion et al.dt=1, Input=E, Learning paradigm=MB2025.06 | 0.98 | 5.5 | |
| Zhu et al.dt=1, Input=E, Learning paradigm=USL2025.06 | 1.02 | 4 | |
| STPBackbone=ViT-S/16, Protocol=Event Pretraining + Fully-Supervised2026.03 | 1.22 | 6.34 | |
| ECDPBackbone=ViT-S/16, Protocol=Event Pretraining + Fully-Supervised2026.03 | 1.26 | 6.69 | |
| ECDPBackbone=ResNet-50, Protocol=Event Pretraining + Fully-Supervised2026.03 | 1.35 | 8.57 | |
| ESTMeasurement=time stamps, Kernel=learnt2019.04 | 1.38 | 8.2 | |
| DCEFlowProtocol=RGB initialization + Fully-Supervised2026.03 | 1.39 | 8.01 | |
| EDCFlowdt=1, Input=E, Learning paradigm=SL2025.06 | 1.49 | 10.79 | |
| ESTMeasurement=time stamps, Kernel=alpha2019.04 | 1.52 | 11.7 | |
| IDNet-4dt=1, Input=E, Learning paradigm=SL, re-implemented=true2025.06 | 1.55 | 10.84 | |
| ESTMeasurement=time stamps, Kernel=exponential2019.04 | 1.58 | 10.5 | |
| EDCFlow (+in3)dt=4, Input=E, Learning paradigm=SL, training_inclusion=flying32025.06 | 1.61 | 11.19 | |
| Voxel GridMeasurement=polarity, Kernel=trilinear2019.04 | 1.65 | 14.6 | |
| IDNet-8dt=1, Input=E, Learning paradigm=SL, re-implemented=true2025.06 | 1.67 | 13.64 | |
| Voxel GridMeasurement=time stamps, Kernel=trilinear2019.04 | 1.7 | 14.3 | |
| ESTMeasurement=time stamps, Kernel=trilinear2019.04 | 1.71 | 11.4 | |
| EV-FlowNetdt=1, Input=E, Learning paradigm=SSL2025.06 | 1.72 | 15.1 | |
| EV-FlowNetMeasurement=count, Kernel=trilinear2019.04 | 1.72 | 15.1 | |
| TMAdt=1, Input=E, Learning paradigm=SL2025.06 | 1.81 | 27.29 | |
| Event FrameMeasurement=time stamps, Kernel=trilinear2019.04 | 1.93 | 18.9 | |
| E-RAFTdt=1, Input=E, Learning paradigm=SL2025.06 | 1.94 | 30.79 | |
| EDCFlow (+in1)dt=4, Input=E, Learning paradigm=SL, training_inclusion=flying12025.06 | 1.97 | 15.56 | |
| Two-Channel ImageMeasurement=time stamps, Kernel=trilinear2019.04 | 1.97 | 14.9 | |
| Two-Channel ImageMeasurement=count, Kernel=trilinear2019.04 | 2.03 | 22.8 | |
| ESTBackbone=ResNet-18, Protocol=RGB initialization + Fully-Supervised2026.03 | 2.05 | 19.9 | |
| EV-MGRFlowNetdt=4, Input=E, Learning paradigm=USL2025.06 | 2.39 | 23.7 | |
| MultiCM (Burgers')dt=4, Input=E, Learning paradigm=MB2025.06 | 2.49 | 26.35 | |
| STE-FlowNetdt=4, Input=E, Learning paradigm=SSL2025.06 | 2.52 | 26.1 | |
| DCEIFlowdt=4, Input=E+I1, Learning paradigm=SL2025.06 | 3.48 | 42.05 | |
| Zhu et al.dt=4, Input=E, Learning paradigm=USL2025.06 | 3.85 | 46.8 | |
| EV-FlowNetdt=4, Input=E, Learning paradigm=SSL2025.06 | 4.05 | 45.3 | |
| EDCFlowdt=4, Input=E, Learning paradigm=SL2025.06 | 4.16 | 44.29 | |
| TMAdt=4, Input=E, Learning paradigm=SL2025.06 | 4.32 | 52.74 | |
| IDNet-4dt=4, Input=E, Learning paradigm=SL, re-implemented=true2025.06 | 4.82 | 56.82 | |
| E-RAFTdt=4, Input=E, Learning paradigm=SL2025.06 | 5.09 | 64.19 | |
| IDNet-8dt=4, Input=E, Learning paradigm=SL, re-implemented=true2025.06 | 6.1 | 72.97 |