We investigate two crucial and closely related aspects of CNNs for optical flow estimation: models and training. First, we design a compact but effective CNN model, called PWC-Net, according to simple and well-established principles: pyramidal processing, warping, and cost volume processing... (read more)
PDFTASK | DATASET | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK | BENCHMARK |
---|---|---|---|---|---|---|
Optical Flow Estimation | KITTI 2012 | PWC-Net + ft - axXiv | Average End-Point Error | 1.5 | # 2 | |
Optical Flow Estimation | KITTI 2015 | PWC-Net + ft - axXiv | Fl-all | 7.72 | # 4 | |
Optical Flow Estimation | Sintel-clean | PWC-Net | Average End-Point Error | 3.45 | # 6 | |
Optical Flow Estimation | Sintel-final | PWC-Net | Average End-Point Error | 4.6 | # 9 |
METHOD | TYPE | |
---|---|---|
🤖 No Methods Found | Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet |