qhjqhj00 optical-flow-eval
Pyramidal Gradient Matching for Optical Flow Estimation — Li (2017) (arXiv:1704.03217, 2017)
What this evaluates
Evaluates dense optical flow estimation by predicting pixel-wise displacement vectors between consecutive frames. It probes robustness to large motions, occlusions, blur, and atmospheric effects across synthetic and real-world driving scenes.
Datasets
- MPI Sintel — total ?; splits: train (2082), test (-1)
- KITTI — total ?; splits: train (194), test (-1)
- Middlebury — total ?; splits: test (12)
Metrics
AEE (Average Endpoint Error)(primary) — range: pixels- Average Euclidean distance between predicted and ground truth flow vectors across specified pixel sets (all, non-occluded, or occluded).
Out-Noc / Out-All— range: percent- Percentage of pixels where the endpoint error exceeds 3 pixels, computed over non-occluded (Out-Noc) or all (Out-All) regions.
Input / output format
Input: Pairs of consecutive images (grayscale or color) from a video sequence or stereo rig.
Output: Dense 2-channel flow field representing horizontal (u) and vertical (v) displacement vectors for every pixel.
Scoring recipe
def compute_metrics(pred_flow, gt_flow, mask_noc=None):
error = np.sqrt((pred_flow[:,:,0] - gt_flow[:,:,0])**2 + (pred_flow[:,:,1] - gt_flow[:,:,1])**2)
if mask_noc is not None:
aee = np.mean(error[mask_noc])
out = np.mean(error[mask_noc] > 3) * 100
else:
aee = np.mean(error)
out = np.mean(error > 3) * 100
return aee, out
Common pitfalls
- AEE is reported separately for non-occluded and all areas; comparing only one can misrepresent robustness.
- MPI Sintel provides 'clean' and 'final' passes with different GT generation; results are not directly comparable across passes.
- Processing time is highly hardware-dependent; the paper notes results are not normalized for CPU/GPU or parallelization.
Evidence (verbatim from paper)
AEE on each pass is computed with all 1041 image pairs of corresponding pass. Out-Noc (ALL) is the percentage of erroneous pixels (>3) in non-occluded (all) areas.
Citation
@misc{li2017pyramidal,
title={Pyramidal Gradient Matching for Optical Flow Estimation},
author={Li (2017)},
year={2017},
note={arXiv:1704.03217}
}
- arXiv: 1704.03217