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ResearchFormerly WizWand

Optical Flow on MVSEC 1.0 (outdoor_day1)

0.22EPEDCEIFlow
0.05641.16072.2653.3693Mar 20, 2020Jan 31, 2021Dec 14, 2021Oct 27, 2022Sep 9, 2023Jul 22, 2024Jun 4, 2025
Updated 8mo ago

Evaluation results

MethodLinks
DCEIFlowdt=1, Input=E+I1, Learning paradigm=SL2025.060.220
EDCFlowdt=1, Input=E, Learning paradigm=SL2025.060.230
E-RAFTdt=1, Input=E, Learning paradigm=SL2025.060.240
TMAdt=1, Input=E, Learning paradigm=SL2025.060.257
EV-MGRFlowNetdt=1, Input=E, Learning paradigm=USL2025.060.282
MultiCM (Burgers')dt=1, Input=E, Learning paradigm=MB2025.060.310
IDNet-4dt=1, Input=E, Learning paradigm=SL, re-implemented=true2025.060.310
Zhu et al.dt=1, Input=E, Learning paradigm=USL2025.060.320
EVFlow+dt=12020.030.320
IDNet-8dt=1, Input=E, Learning paradigm=SL, re-implemented=true2025.060.331
ECN*dt=1, training_split=80%, evaluation_split=20%2020.030.354
STE-FlowNetdt=1, Input=E, Learning paradigm=SSL2025.060.420
EV-FlowNetdt=1, Input=E, Learning paradigm=SSL2025.060.4920
EVFlowdt=12020.030.4920
Brebion et al.dt=1, Input=E, Learning paradigm=MB2025.060.5320
EDCFlowdt=4, Input=E, Learning paradigm=SL2025.060.6785
Oursdt=12020.030.6899
TMAdt=4, Input=E, Learning paradigm=SL2025.060.7108
E-RAFTdt=4, Input=E, Learning paradigm=SL2025.060.72112
DCEIFlowdt=4, Input=E+I1, Learning paradigm=SL2025.060.89319
STE-FlowNetdt=4, Input=E, Learning paradigm=SSL2025.060.99390
EV-MGRFlowNetdt=4, Input=E, Learning paradigm=USL2025.061.1622
EV-FlowNetdt=4, Input=E, Learning paradigm=SSL2025.061.23730
MultiCM (Burgers')dt=4, Input=E, Learning paradigm=MB2025.061.25921
IDNet-8dt=4, Input=E, Learning paradigm=SL, re-implemented=true2025.061.26491
Zhu et al.dt=4, Input=E, Learning paradigm=USL2025.061.3970
IDNet-4dt=4, Input=E, Learning paradigm=SL, re-implemented=true2025.061.3248
Zerosdt=1, description=Baseline error resulting from always estimating zero flow2020.034.3139