3d-obj-det-seg-eval

name	3d-obj-det-seg-eval
description	Evaluates a model's ability to detect and segment 3D objects in indoor scenes using point cloud inputs. It probes spatial reasoning and instance-level understanding by measuring how well the model generalizes from synthetic internet-scale data to real-world scanned environments. Use when the user wants to benchmark on ScanNet, SceneVerse++, or asks about evaluating this task. Reports AP.
metadata	{"skill_kind":"dataset_eval","source_arxiv":2604.01907,"bibtex_key":"chen2026lifting","confidence":"high"}

3d-obj-det-seg-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 detect and segment 3D objects in indoor scenes using point cloud inputs. It probes spatial reasoning and instance-level understanding by measuring how well the model generalizes from synthetic internet-scale data to real-world scanned environments.

Datasets

• ScanNet — total ?; splits: test (-1); repo https://github.com/ScanNet/ScanNet
• SceneVerse++ — total ?; splits: train (-1)

Metrics

• AP25 — range: [0, 1]
  • Average Precision at Intersection over Union (IoU) threshold of 0.25.
• AP50 — range: [0, 1]
  • Average Precision at IoU threshold of 0.50.
• AP (primary) — range: [0, 1]
  • Average Precision averaged across IoU thresholds from 0.25 to 0.75 in 0.05 increments.

Input / output format

Input: 3D point clouds (often cropped to a 3-meter radius around a target object) with optional graph-based segmentation masks.

Output: Bounding boxes and instance segmentation masks for 15 semantic categories.

Scoring recipe

for each scene:
  preds = model.predict(point_cloud)
  for each pred_box:
    match with ground_truth_box
    if IoU(pred, gt) >= threshold:
      mark as true positive
  compute precision-recall curve
  interpolate AP at 101 recall levels
return mean AP across categories

Common pitfalls

• Model performance is highly sensitive to the hyperparameters of the graph-based segmentation method (kThresh, segMinVerts) used to generate input segments.
• Distribution shift between synthetic training data (SceneVerse++) and real scanned data (ScanNet) causes significant performance degradation if not carefully managed.

Evidence (verbatim from paper)

The AP is reported as the average over the whole ScanNet.

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