license: cc-by-nc-4.0
tags:
- 3d-object-detection
- incremental-learning
- point-cloud
- scannet
- sunrgbd
annotations_creators:
- found
language: []
pretty_name: LDMR dataset metadata (ScanNetV2 + SUN RGB-D annotation indices)
LDMR — dataset metadata
Annotation-index files (.pkl) for
Breaking the Model Forgetting Cycle in Long-Incremental 3D Object Detection Peisheng Qian, Jie Xu, Xulei Yang, Na Zhao European Conference on Computer Vision (ECCV), 2026
Code: https://github.com/qianpeisheng/LDMR · Checkpoints: Peisheng/LDMR
What this is — and what it is not
These are annotation indices only: per-scene bounding boxes, class ids, and relative paths pointing at point-cloud files you must produce yourself. They contain no scan data, no point clouds, and no images.
This repository does not redistribute ScanNet or SUN RGB-D. ScanNet requires each user to sign its Terms of Use directly with the ScanNet team, so mirroring the scans — or point clouds derived from them — would breach those terms.
To use these files you must download the scans from their original sources and extract them yourself:
- ScanNetV2 — http://www.scan-net.org/ (requires signing the Terms of Use)
- SUN RGB-D — https://rgbd.cs.princeton.edu/
The point of publishing them is to let you skip the index-building step of data preparation, and to pin the exact annotation set behind the paper's numbers. The per-scene extraction step is still required.
Contents
~37 MB.
| File | Scenes | annos['class'] range |
Notes |
|---|---|---|---|
scannet/scannet_infos_train_40class_corrected.pkl |
1201 | 1–40 | class ids corrected to 1-based NYU40 |
scannet/scannet_infos_val_40class_corrected.pkl |
312 | 1–40 | " |
scannet/scannet_infos_test_40class_corrected.pkl |
100 | — | no annotations (test split) |
sunrgbd/sunrgbd_infos_train_40class.pkl |
5285 | 0–39 | 478 scenes have gt_num == 0 |
sunrgbd/sunrgbd_infos_val_40class.pkl |
5050 | 0–39 | 436 scenes have gt_num == 0 |
Each file unpickles to a list[dict], one entry per scene, with point_cloud
and a relative pts_path (e.g. points/scene0191_00.bin). Except for the
ScanNet test split, each entry also has an annos block holding
gt_boxes_upright_depth, class, gt_num and related fields. ScanNet entries
additionally carry axis_align_matrix, pts_instance_mask_path and
pts_semantic_mask_path; SUN RGB-D entries carry calib and image.
Two details will bite you if you read these files directly.
The two datasets use different class-id bases. The _corrected suffix on the
ScanNet files is load-bearing: tools/create_data.py writes annos['class'] as
0-based indices, whereas the class mappings and valid_cat_ids throughout the
LDMR codebase treat a ScanNet class id as the NYU40 id itself, which is 1-based.
These files have the shift applied, by
tools/data_converter/scannet_correct_class_ids.py; using uncorrected indices
silently mislabels every box by one class. The SUN RGB-D files are 0-based (0–39)
and need no such correction — that asymmetry is expected, and the configs handle
each dataset on its own terms.
Object-free scenes omit the annotation arrays. In SUN RGB-D, 478 train and
436 val scenes have gt_num == 0; for those the annos block has no class or
gt_boxes_upright_depth key at all, rather than an empty array. Guard with
if 'class' in info['annos'] when iterating.
Usage
from huggingface_hub import snapshot_download
snapshot_download('Peisheng/LDMR-data', repo_type='dataset', local_dir='data')
The LDMR configs expect the files at:
data/scannet/scannet_infos_{train,val,test}_40class_corrected.pkl
data/sunrgbd/sunrgbd_infos_{train,val}_40class.pkl
alongside the point clouds you extracted from the original scans. See the dataset-preparation section of the GitHub README.
License
The index files are released under CC BY-NC 4.0, matching the LDMR codebase. They are derived from the ScanNetV2 and SUN RGB-D annotations; the original datasets remain governed by their own licenses and terms of use, which you accept with their respective providers. Non-commercial use only.
Citation
@inproceedings{qian2026ldmr,
title = {Breaking the Model Forgetting Cycle in Long-Incremental 3D Object Detection},
author = {Qian, Peisheng and Xu, Jie and Yang, Xulei and Zhao, Na},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2026}
}
Please also cite ScanNet and SUN RGB-D if you use these files.