Table 2:
Number of trainable parameters and computational speed of processing Sintel image sequences for lightweight and heavyweight models. Runtime is measured using Sintel image sequences with a frame size of 1024 × 436 pixels. General speed differences between the computational frameworks such as Tensorflow and Caffe should be considered when comparing the run times of various networks.
| Method | Number of layers | Number of parameters (M) | Framework | GPU (NVIDIA) | Time (ms) | FPS |
|---|---|---|---|---|---|---|
| DDCNet-B0 | 31 | 1.03 | TF2 | Quadro RTX 8000 | 76 | 13 |
| DDCNet-B1 | 30 | 2.99 | TF2 Possible Caffe |
Quadro RTX 8000 Quadro RTX 8000 |
30 ≈7 |
33 142 |
|
| ||||||
| FlowNet Simple | 17 | 38 | TF1 Caffe |
Tesla K80 GTX 1080 |
86 18 |
11 55 |
| FlowNet Correlation | 26 | 39.16 | TF1 Caffe |
Tesla K80 GTX 1080 |
179 32 |
5 31 |
| FlowNet2 | 115 | 162.49 | TF1 Caffe |
Tesla K80 GTX 1080 |
692 123 |
1 8 |
|
| ||||||
| LiteFlowNet | 94 | 5.37 | Caffe | GTX 1080 | 88.53 | 12 |
| SPyNet | 35 | 1.2 | Torch | GTX 1080 | 129.83 | 8 |
| PWC-Net+ | 59 | 8.75 | Caffe | TITAN Xp | 39.63 | 25 |
|
| ||||||
| CA-DCNN | - | - | Caffe | RTX 1080Ti | 480 | - |
| RAFT | - | 5.3 | PyTorch | RTX 2080Ti | 100 | - |
| GMA | - | 5.9 | PyTorch | RTX 2080Ti | 73 | - |
| SAMFL | - | 10.45 | PyTorch | - | - | - |
| PMC-PWC | - | 7.86 | PyTorch | RTX 1080Ti | 200 | - |