jadgardner/neusky-nerfosr-overlay

NeuSky Additions for NeRF-OSR

This repository contains the additional files used to train and evaluate NeuSky on three scenes from the NeRF-OSR dataset:

• Cityscapes semantic segmentation masks;
• scene point clouds used for SfM-based centring and scaling;
• rotations that align the captured environment maps with the dataset camera coordinate frame.

It is an overlay for an official NeRF-OSR download. It does not contain NeRF-OSR RGB images, camera poses, environment maps, COLMAP models, or any other original dataset files.

Property	Value
Scenes	3
Segmentation masks	1,021
Point clouds	3
Environment-map rotation records	16
Release	1.0
Release date	2026-07-30
NeuSky commit	928d65859e97fa15778cfe7138e06fd8ea0fc496

Contents

Each independently downloadable archive preserves the target paths within the official dataset:

Data/<scene>/final/
  points3d.ply
  envmap_rotations.json
  train/cityscapes_mask/*.png
  validation/cityscapes_mask/*.png
  test/cityscapes_mask/*.png

The included scenes and mask counts are:

Scene	Train	Validation	Test	Total
lk2	160	5	95	260
lwp	258	5	96	359
st	301	5	96	402

Every mask has the same filename stem as its corresponding official RGB image. The masks use the Cityscapes palette consumed by the NeuSky dataparser.

The binary PLY files contain XYZ and RGB values. They were converted from COLMAP point clouds triangulated against the known, dataset-normalised NeRF-OSR camera poses. NeuSky uses them to choose a robust scene centre and scale while keeping scene geometry and cameras inside the unit sky sphere.

Each envmap_rotations.json record maps directions in the associated equirectangular environment image into the dataset-normalised world frame. Fifteen rotations were recovered by registering perspective crops from the environment panoramas into the corresponding scene model. The remaining st/01-09_14_00 rotation was obtained by panorama-to-panorama composition and is marked with its method and diagnostics in the JSON.

Download and Install

First obtain the NeRF-OSR dataset from its official project page. Then download and verify this overlay:

hf download jadgardner/neusky-nerfosr-overlay \
  --repo-type dataset \
  --revision v1.0 \
  --local-dir neusky-nerfosr-overlay

(cd neusky-nerfosr-overlay && sha256sum -c SHA256SUMS)

Extract the archives over the directory that contains the official Data/ folder:

for archive in neusky-nerfosr-overlay/archives/*.tar.zst; do
  tar --zstd -xf "$archive" -C /path/to/NeRF-OSR
done

Download a single scene:

hf download jadgardner/neusky-nerfosr-overlay \
  --repo-type dataset \
  --revision v1.0 \
  archives/lk2.tar.zst \
  --local-dir neusky-nerfosr-overlay

tar --zstd -xf \
  neusky-nerfosr-overlay/archives/lk2.tar.zst \
  -C /path/to/NeRF-OSR

An individual archive can also be downloaded without the CLI:

curl -L \
  "https://huggingface.co/datasets/jadgardner/neusky-nerfosr-overlay/resolve/v1.0/archives/lk2.tar.zst?download=true" \
  -o lk2.tar.zst

zstd is required for extraction. OVERLAY_CONTENTS.json records the destination, size and SHA256 of every extracted file. MANIFEST.json and SHA256SUMS cover the downloadable release files.

Reproducibility

The relevant NeuSky scripts are:

• scripts/nerfosr_pointclouds.py, which converts the registered COLMAP points to the shipped PLY format;
• scripts/register_envmaps.py, which recovers, inspects and installs the environment-map rotations;
• scripts/nerfosr_overlay/build_hf_release.py, which validates the exact additions and builds this allowlisted release.

The release builder checks that every mask corresponds to an official RGB filename, validates the PLY headers and rotation matrices, and stages only the three additions listed above.

Licence

The NeuSky-created masks, point clouds and rotation metadata in this overlay are licensed under CC BY 4.0. This licence does not apply to the original NeRF-OSR dataset, which is not included and remains subject to its own terms. See LICENSE.md.

Citation

Please cite both NeuSky and NeRF-OSR when using these additions:

@inproceedings{gardner2024neusky,
  title     = {The Sky's the Limit: Relightable Outdoor Scenes via a
               Sky-pixel Constrained Illumination Prior and Outside-In
               Visibility},
  author    = {Gardner, James A. D. and Egger, Bernhard and Smith, William A. P.},
  booktitle = {European Conference on Computer Vision},
  year      = {2024}
}

@inproceedings{rudnev2022nerfosr,
  title     = {{NeRF} for Outdoor Scene Relighting},
  author    = {Rudnev, Viktor and Elgharib, Mohamed and Smith, William and
               Liu, Lingjie and Golyanik, Vladislav and Theobalt, Christian},
  booktitle = {European Conference on Computer Vision},
  year      = {2022}
}