Optical Flow Estimation On Kitti 2015
Metriken
Average End-Point Error
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Average End-Point Error |
|---|---|
| perceiver-io-a-general-architecture-for | 4.98 |
| improved-cross-view-completion-pre-training | - |
| a-lightweight-optical-flow-cnn-revisiting | - |
| recurrent-partial-kernel-network-for | - |
| maskflownet-asymmetric-feature-matching-with | - |
| dpflow-adaptive-optical-flow-estimation-with-1 | - |
| unsupervised-optical-flow-using-cost-function | - |
| liteflownet-a-lightweight-convolutional | - |
| maskflownet-asymmetric-feature-matching-with | - |
| liteflownet3-resolving-correspondence | - |
| global-matching-with-overlapping-attention | - |
| selflow-self-supervised-learning-of-optical | - |
| learning-optical-flow-and-scene-flow-with | - |
| rapidflow-recurrent-adaptable-pyramids-with | - |
| fastflownet-a-lightweight-network-for-fast | - |
| iterative-residual-refinement-for-joint | - |
| liteflownet3-resolving-correspondence | - |