README.md

metadata

pretty_name: nuScenes-NRS
description: >-
  Derived front-camera road-segmentation masks for the low-light nuScenes-NRS
  benchmark.
language:
  - en
license: other
task_categories:
  - image-segmentation
tags:
  - autonomous-driving
  - road-segmentation
  - low-light-perception
  - nuscenes
size_categories:
  - 1K<n<10K

nuScenes-NRS

nuScenes-NRS (nuScenes Nighttime Road Segmentation) is the road-segmentation label release used by IAF-Net: Illumination-Adaptive Fusion for Low-Light Urban Road Segmentation. This repository contains the derived labels and the information needed to reproduce them from an authorized copy of the official nuScenes data.

What is included

split	scenes	masks	mask resolution	distribution
training	79	3,182	1600 x 900	training/masks.zip
validation	20	805	1600 x 900	validation/masks.zip
total	99	3,987

The split files contain one nuScenes sample token per line. Each archive contains a masks/ directory with files named <sample-token>.png; a mask corresponds to the CAM_FRONT key frame of that sample. The training and validation scene sets are disjoint. The release contains no RGB images, LiDAR point clouds, lidarseg files, depth maps, normal maps, calibration files, or other raw nuScenes sensor data.

Extract the archives from the repository root before using the validator or a loader that expects the unpacked layout:

unzip training/masks.zip -d training
unzip validation/masks.zip -d validation

Label format

Each file is an 8-bit, three-channel PNG with shape 900 x 1600 x 3 when read as RGB:

• road: R=255, G=0, B=0;
• background: R=G=B=0.

For a binary mask, use mask = (rgb[..., 0] > 127). When reading with OpenCV (cv2.imread), the road value is in channel 2 because OpenCV uses BGR order. The labels are intentionally stored in the same encoding as the training protocol used in IAF-Net.

Provenance and generation

The labels are projected from the official nuScenes LIDAR_TOP point cloud and its lidarseg labels (class 24, drivable_surface) into the official CAM_FRONT camera. The historical post-processing pipeline is:

1. transform LiDAR points through the calibrated-sensor and ego-pose chains;
2. retain points in front of the camera and inside the 1600 x 900 image;
3. Delaunay triangulation with a maximum projected triangle edge of 40 px;
4. elliptical morphological closing (15 x 15, two iterations);
5. external-contour Douglas--Peucker approximation (epsilon = 0.01 of contour perimeter), ignoring contours smaller than 1,000 px;
6. a final 5 x 5 elliptical erosion (one iteration).

The reference implementation is scripts/generate_masks_from_nuscenes.py. It writes a three-channel PNG with the road in the red channel and never modifies the source dataset. Because the original nuScenes files are not redistributed here, exact regeneration requires an authorized copy of the same nuScenes trainval and lidarseg release.

Reproduce the masks

Obtain v1.0-trainval and the matching lidarseg package from the official nuScenes download page, and accept the nuScenes terms before use. Then install the dependencies:

python -m pip install -r requirements.txt

Generate either split (the command below regenerates training; replace the split name and file for validation):

python scripts/generate_masks_from_nuscenes.py \
  --dataroot /path/to/nuScenes \
  --version v1.0-trainval \
  --split training \
  --split-file splits/training.txt \
  --output-root /tmp/nuScenes-NRS-regenerated

The output is written to /tmp/nuScenes-NRS-regenerated/training/masks/. The script reconstructs the official CAM_FRONT/LIDAR_TOP key-frame mapping from sample_data.json, checks for missing or duplicate records, and reports missing lidarseg files instead of silently changing the split.

Verify a downloaded release

From the repository root, either extract both archives as shown above and run the unpacked validator:

python scripts/validate_release.py --root .

or verify the archive contents and checksums directly:

python scripts/validate_archives.py --root .

The checks cover split counts, filename/token consistency, PNG shape, dtype, channel encoding, duplicate tokens, scene disjointness (when official metadata is supplied), archive contents, and the recorded SHA-256 manifest.

Download

The repository is public at https://huggingface.co/datasets/PeterNano/nuScenes-NRS. For a command-line download:

pip install -U huggingface_hub
hf download PeterNano/nuScenes-NRS \
  --repo-type dataset --local-dir nuScenes-NRS

Older huggingface_hub versions expose the same command as huggingface-cli download.

License and data-use notice

This repository deliberately redistributes only derived masks, split/token metadata, documentation, and scripts. It does not redistribute any original nuScenes sensor or annotation files. The official nuScenes terms state that use of the dataset and of data derived from it is governed by the non-commercial CC BY-NC-SA 4.0 license together with additional Dataset Terms; where the two texts conflict, the Dataset Terms prevail. See the official terms of use and the CC BY-NC-SA 4.0 legal code. Users must obtain the original nuScenes release directly and satisfy those terms. The masks and split metadata are offered only to the extent permitted by those terms, with the additional boundary notes in LICENSE_DATA.md. The scripts are MIT-licensed independently. No license in this repository grants rights to the original nuScenes data, trademarks, or third-party components.

The labels are automatically generated from sparse projected LiDAR semantics and are not hand-drawn dense annotations. They can contain holes, boundary uncertainty, and projection artifacts, especially in very dark or occluded regions. They should therefore be used as benchmark labels rather than as a survey-grade map of drivable space.

Citation

If you use nuScenes-NRS or the accompanying masks, please cite the dataset record and the IAF-Net paper. A machine-readable entry is provided in CITATION.cff.

@misc{anonymous2026iafnet,
  title        = {IAF-Net: Illumination-Adaptive Fusion for Low-Light Urban Road Segmentation},
  author       = {{Anonymous Authors}},
  year         = {2026},
  howpublished = {Hugging Face Dataset Card: PeterNano/nuScenes-NRS},
  note         = {nuScenes-NRS derived-label release, version 1.0.0}
}