Peisheng/LDMR-data

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.