Streaming 3d Reconstruction Eval

---
name: streaming-3d-reconstruction-eval
description: Evaluates a model's ability to perform streaming camera pose estimation and 3D reconstruction over long video sequences. It probes long-range geometric consistency, drift resistance, and reconstruction fidelity across diverse indoor and outdoor environments. Use when the user wants to benchmark on Oxford Spires, ETH3D, 7-Scenes, Tanks and Temples, NRGBD, or asks about evaluating this task. Reports ATE, F1.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2604.14141
  bibtex_key: [REDACTED]
  confidence: high
---

# streaming-3d-reconstruction-eval

> Geometric Context Transformer for Streaming 3D Reconstruction — Chen et al. (2026) (arXiv:2604.14141, 2026)

## What this evaluates

Evaluates a model's ability to perform streaming camera pose estimation and 3D reconstruction over long video sequences. It probes long-range geometric consistency, drift resistance, and reconstruction fidelity across diverse indoor and outdoor environments.

## Datasets

- **Oxford Spires** — total ?; splits: test (-1)
- **ETH3D** — total ?; splits: test (-1)
- **7-Scenes** — total ?; splits: test (-1)
- **Tanks and Temples** — total ?; splits: test (-1)
- **NRGBD** — total ?; splits: test (-1)

## Metrics

- `ATE` **(primary)** — range: meters
  - Absolute Trajectory Error, measuring the mean translation error between predicted and ground-truth camera poses after rigid alignment.
- `AUC@15` — range: percent
  - Area Under the Curve of the cumulative pose error plot up to a 15-meter threshold.
- `F1` **(primary)** — range: percent
  - F1 score for 3D reconstruction, computed as the harmonic mean of Accuracy and Completeness metrics.

## Input / output format

**Input**: Causal sequence of RGB frames processed in a streaming fashion without access to future frames.

**Output**: Per-frame predicted camera poses (6-DoF) and/or a temporally consistent 3D point cloud.

## Scoring recipe

```python
def compute_ate(pred_poses, gt_poses):
    aligned = align_se3(pred_poses, gt_poses)
    return np.mean([np.linalg.norm(p - g) for p, g in zip(aligned, gt_poses)])

def compute_f1(pred_pts, gt_pts):
    acc = chamfer(pred_pts, gt_pts)
    comp = chamfer(gt_pts, pred_pts)
    return 2 * (acc * comp) / (acc + comp) * 100
```

## Common pitfalls

- Streaming methods must not reset internal state between sequences for fair comparison.
- Oxford Spires has sparse (320 frames) and dense (3,840 frames) settings that test different aspects of drift.
- RPE-Rot is measured in degrees while ATE and RPE-trans are in meters.

## Evidence (verbatim from paper)

> We evaluate under two settings to test both aspects. In the sparse setting (320 frames, sampled every 12 frames), all method categories can run on our hardware, enabling a fair comparison across offline, optimization-based, and online approaches. As shown in Table 2, LingBot-Map achieves the best results on nearly all metrics. Despite operating in a streaming online manner, our method surpasses the strongest offline baselines by a large margin. LingBot-Map achieves an AUC@15 of 61.64, substantially exceeding the best offline method DA3 (49.84) and more than doubling VGGT (23.84). On trajectory-level accuracy, LingBot-Map reduces ATE from 12.87 (DA3) and 24.78 (VGGT) to 6.42.

## Citation

```bibtex
@misc{chen2026geometric,
  title={Geometric Context Transformer for Streaming 3D Reconstruction},
  author={Chen et al. (2026)},
  year={2026},
  note={arXiv:2604.14141}
}
```

- arXiv: 2604.14141