3d Vln Eval

3d-vln-eval

Lifting Unlabeled Internet-level Data for 3D Scene Understanding — Chen et al. (2026) (arXiv:2604.01907, 2026)

What this evaluates

Evaluates a model's ability to navigate 3D environments based on natural language instructions. It measures path efficiency, success in reaching targets, and robustness to scale calibration and data distribution shifts.

Datasets

• R2R — total ?; splits: train (-1); repo https://github.com/peteanderson80/boston-pathway
• NaVILA — total 20000; splits: train (20000)
• SceneVerse++ VLN — total ?; splits: train (-1)

Metrics

• SR (primary) — range: [0, 1]
  • Success Rate: fraction of episodes where the agent reaches within a threshold distance of the target.
• SPL — range: [0, 1]
  • Success weighted by Path Length: SR multiplied by the ratio of optimal path length to actual path length.
• Dist — range: [0, inf)
  • Distance-to-Goal: average Euclidean distance between the agent's final position and the target upon episode termination.

Input / output format

Input: Camera trajectories, depth maps, and natural language navigation instructions.

Output: Sequence of navigation actions (e.g., forward, turn left/right) or trajectory waypoints.

Scoring recipe

sr = 0
spl_sum = 0
for episode in episodes:
    traj = model.navigate(instruction)
    if distance(traj.end, target) <= threshold:
        sr += 1
        optimal_len = compute_optimal_path(instruction)
        spl_sum += (optimal_len / len(traj))
sr = sr / len(episodes)
spl = spl_sum / len(episodes)
return sr, spl

Common pitfalls

• Depth scale calibration is critical; arbitrary SfM scales must be converted to real-world meters using furniture-based monocular depth estimates.
• Label rebalancing during mixed-training can oversample rare actions, potentially biasing navigation behavior.

Evidence (verbatim from paper)

We evaluate two settings, zero-shot and mixed-training with R2R, and report results in [Tab. S.5]. ... SR↑ | OS↑ | SPL↑ | Dist↓ | PL

Citation

@misc{chen2026lifting,
  title={Lifting Unlabeled Internet-level Data for 3D Scene Understanding},
  author={Chen et al. (2026)},
  year={2026},
  note={arXiv:2604.01907}
}

• arXiv: 2604.01907