Ueof Eval

ueof-eval

UEOF: A Benchmark Dataset for Underwater Event-Based Optical Flow — Truong et al. (2026) (arXiv:2601.10054, 2026)

What this evaluates

Evaluates the accuracy of event-based optical flow estimation models in underwater environments. It probes how well algorithms handle low-texture, turbid, and refractive conditions compared to terrestrial benchmarks.

Datasets

• UEOF — total ?; splits: test (5)

Metrics

• AEE (primary) — range: pixels
  • Average Endpoint Error in pixels. Computed as the mean Euclidean distance between predicted and ground-truth optical flow vectors across all pixels.
• ANPE@N — range: percent
  • N-pixel error rate. Represents the percentage of pixels with an endpoint error lower than N pixels for N ∈ {1, 2, 3, 5, 10, 20}. Lower values indicate better performance.

Input / output format

Input: Event stream data (and optionally RGB frames or camera ego-velocity for multimodal baselines). Models process accumulated event windows over time.

Output: 2D optical flow vectors (u, v) per pixel for each frame in the sequence.

Scoring recipe

def compute_metrics(pred_flow, gt_flow):
    epe = np.linalg.norm(pred_flow - gt_flow, axis=-1)
    aee = np.mean(epe)
    anpe = {}
    for n in [1, 2, 3, 5, 10, 20]:
        anpe[f'ANPE@{n}'] = np.mean(epe > n) * 100
    return aee, anpe

Common pitfalls

• Models pretrained on terrestrial datasets (e.g., DSEC) suffer severe feature distribution shifts in underwater scenes due to low-frequency textures and turbidity.
• Contrast maximization methods fail under refractive caustics, mistaking light patterns for motion events.
• Deep-water scenes introduce haloing, attenuation, and low contrast, significantly increasing error rates compared to shallow-water scenes.

Evidence (verbatim from paper)

The event-based optical flow metrics include the average endpoint error (AEE) as well as ANPE, which represents the percentage of pixels with an endpoint error lower than N pixels for N ∈ {1, 2, 3, 5, 10, 20}.

Citation

@misc{truong2026ueof,
  title={UEOF: A Benchmark Dataset for Underwater Event-Based Optical Flow},
  author={Truong et al. (2026)},
  year={2026},
  note={arXiv:2601.10054}
}

• arXiv: 2601.10054