Optical Flow Estimation On Sintel Clean
評価指標
Average End-Point Error
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
比較表
| モデル名 | Average End-Point Error |
|---|---|
| fdflownet-fast-optical-flow-estimation-using | 3.71 |
| rapidflow-recurrent-adaptable-pyramids-with | 2.03 |
| scopeflow-dynamic-scene-scoping-for-optical | 3.592 |
| a-lightweight-optical-flow-cnn-revisiting | 3.48 |
| maskflownet-asymmetric-feature-matching-with | 2.52 |
| deep-equilibrium-optical-flow-estimation | 1.519 |
| unifying-flow-stereo-and-depth-estimation | 1.03 |
| recurrent-partial-kernel-network-for | 1.315 |
| raft-recurrent-all-pairs-field-transforms-for | 1.609 |
| iterative-residual-refinement-for-joint | 3.84 |
| selflow-self-supervised-learning-of-optical | 3.74 |
| learning-to-estimate-hidden-motions-with | 1.388 |
| liteflownet3-resolving-correspondence | 3.03 |
| rethinking-raft-for-efficient-optical-flow | 1.27 |
| proflow-learning-to-predict-optical-flow | 2.82 |
| unsupervised-optical-flow-using-cost-function | 4.69 |
| flownet-20-evolution-of-optical-flow | 3.96 |
| perceiver-io-a-general-architecture-for | 1.81 |
| flowformer-a-transformer-architecture-for | 1.16 |
| fastflownet-a-lightweight-network-for-fast | 4.89 |
| optical-flow-estimation-using-a-spatial | 6.64 |
| global-matching-with-overlapping-attention | 1.390 |
| optical-flow-in-mostly-rigid-scenes | 2.53 |
| maskflownet-asymmetric-feature-matching-with | 2.77 |
| liteflownet3-resolving-correspondence | 2.99 |
| improved-cross-view-completion-pre-training | 1.092 |
| dpflow-adaptive-optical-flow-estimation-with-1 | 1.046 |
| liteflownet-a-lightweight-convolutional | 4.54 |