# Kitti Depth Flow Pose Eval

> Evaluates a model's ability to jointly estimate monocular depth, optical flow, and camera ego-motion from consecutive video frames in driving scenes. It probes geometric consistency, motion handling, and self-supervised learning robustness on standard autonomous driving benchmarks. Use when the user wants to benchmark on KITTI Raw, KITTI Flow 2012, KITTI Flow 2015, KITTI Odometry, KITTI Eigen Split, or asks about evaluating this task. Reports EPE.

- Skill: `qhjqhj00/kitti-depth-flow-pose-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/kitti-depth-flow-pose-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/kitti-depth-flow-pose-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/kitti-depth-flow-pose-eval

---


# kitti-depth-flow-pose-eval

> Unsupervised Joint Learning of Depth, Optical Flow, Ego-motion from Video — Li et al. (2021) (arXiv:2105.14520, 2021)

## What this evaluates

Evaluates a model's ability to jointly estimate monocular depth, optical flow, and camera ego-motion from consecutive video frames in driving scenes. It probes geometric consistency, motion handling, and self-supervised learning robustness on standard autonomous driving benchmarks.

## Datasets

- **KITTI Raw** — total 44686; splits: train (40864), val (3822)
- **KITTI Flow 2012** — total ?; splits: test (-1); repo http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=flow
- **KITTI Flow 2015** — total ?; splits: test (-1); repo http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow
- **KITTI Odometry** — total ?; splits: test_seq09 (-1), test_seq10 (-1)
- **KITTI Eigen Split** — total ?; splits: test (-1)

## Metrics

- `EPE` **(primary)** — range: other
  - Average End-Point Error computed over non-occluded (noc), occluded (occ), and all regions.
- `odometry error` — range: other
  - Average translation and rotation error per trajectory segment, following the SfMLearner protocol.
- `depth error` — range: other
  - Standard monocular depth metrics (e.g., absolute relative error, RMSE) evaluated on the KITTI Eigen split.

## Input / output format

**Input**: Consecutive pairs of RGB images from KITTI driving videos, down-sampled to 832x256 resolution.

**Output**: Predicted monocular depth map, optical flow field between frames, and 6-DoF camera ego-motion (translation and rotation).

## Scoring recipe

```python
def compute_epe(flow_pred, flow_gt, region_mask):
    # region_mask: boolean array for noc, occ, or all pixels
    diff = flow_pred[region_mask] - flow_gt[region_mask]
    epe = np.sqrt(np.sum(diff**2, axis=1))
    return np.mean(epe)

# For odometry: compute per-segment translation/rotation error per SfMLearner protocol
# For depth: compute standard metrics (AbsRel, RMSE, etc.) on KITTI Eigen split
```

## Common pitfalls

- Failing to mask dynamic/moving objects during loss computation, which biases depth and flow estimates.
- Using incorrect evaluation splits (e.g., not using KITTI Eigen split for depth or seq 09/10 for pose).
- Ignoring the 3-stage progressive training schedule with specific loss weights, which significantly impacts final convergence and metric scores.

## Evidence (verbatim from paper)

> We report the performance using the average end-point error (EPE) over non-occluded regions (noc), occluded regions(occ) and overall regions (all).

## Citation

```bibtex
@misc{li2021unsupervisedjoint,
  title={Unsupervised Joint Learning of Depth, Optical Flow, Ego-motion from Video},
  author={Li et al. (2021)},
  year={2021},
  note={arXiv:2105.14520}
}
```

- arXiv: 2105.14520