# Depth Anything Ac Eval

> Evaluates zero-shot monocular relative depth estimation robustness under complex environmental conditions such as low light, adverse weather (rain, fog, snow), and synthetic noise. It probes the model's ability to recover fine-grained spatial relationships and object boundaries from degraded inputs without fine-tuning. Use when the user wants to benchmark on DA-2K (multi-condition), NuScenes-night, Robotcar-night, Driving-Stereo, KITTI-C, KITTI, NYU-D, Sintel, ETH3D, DIODE, or asks about evaluating this task. Reports AbsRel, δ1.

- Skill: `qhjqhj00/depth-anything-ac-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/depth-anything-ac-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/depth-anything-ac-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/depth-anything-ac-eval

---


# depth-anything-ac-eval

> Depth Anything at Any Condition — Sun et al. (2025) (arXiv:2507.01634, 2025)

## What this evaluates

Evaluates zero-shot monocular relative depth estimation robustness under complex environmental conditions such as low light, adverse weather (rain, fog, snow), and synthetic noise. It probes the model's ability to recover fine-grained spatial relationships and object boundaries from degraded inputs without fine-tuning.

## Datasets

- **DA-2K (multi-condition)** — total ?; splits: test (-1)
- **NuScenes-night** — total ?; splits: test (-1)
- **Robotcar-night** — total ?; splits: test (-1)
- **Driving-Stereo** — total ?; splits: test (-1)
- **KITTI-C** — total ?; splits: test (-1)
- **KITTI** — total ?; splits: test (-1)
- **NYU-D** — total ?; splits: test (-1)
- **Sintel** — total ?; splits: test (-1)
- **ETH3D** — total ?; splits: test (-1)
- **DIODE** — total ?; splits: test (-1)

## Metrics

- `AbsRel` **(primary)** — range: [0, 1]
  - Mean absolute relative error: (1/N) Σ |d_pred - d_gt| / d_gt. Lower is better.
- `δ1` **(primary)** — range: [0, 1]
  - Percentage of pixels where max(d_pred/d_gt, d_gt/d_pred) < 1.25. Higher is better.
- `Acc` — range: [0, 1]
  - Accuracy of pairwise relative depth ordering on the DA-2K benchmark. Higher is better.

## Input / output format

**Input**: Single RGB image (often subjected to synthetic perturbations like darkness, fog, snow, motion blur, or Gaussian noise for robustness evaluation).

**Output**: Predicted relative depth map (affine-invariant scale).

## Scoring recipe

```python
def compute_metrics(pred, gt):
    # pred, gt: 2D arrays of predicted and ground-truth relative depth
    # Ensure scale alignment if necessary (e.g., median ratio)
    abs_rel = np.mean(np.abs(pred - gt) / gt)
    ratio = np.maximum(pred / gt, gt / pred)
    delta1 = np.mean(ratio < 1.25)
    return {'AbsRel': abs_rel, 'δ1': delta1}
```

## Common pitfalls

- Ground truth from LiDAR/depth cameras is often sparse and noisy, which limits fine-grained evaluation and can artificially cap metric scores.
- Models predict affine-invariant/relative depth, so absolute scale cannot be directly compared without proper alignment or scaling.
- DA-2K uses pairwise relative depth accuracy, which evaluates ordering rather than pixel-wise regression, making it incomparable to AbsRel/δ1.

## Evidence (verbatim from paper)

> For instance, compared to Depth Anything V2, our DepthAnything-AC achieves 0.037 improvements of δ1 metric on the Robotcar-night benchmark.

## Citation

```bibtex
@misc{sun2025depthanythingac,
  title={Depth Anything at Any Condition},
  author={Sun et al. (2025)},
  year={2025},
  note={arXiv:2507.01634}
}
```

- arXiv: 2507.01634