qihoo360/RevealLayer-100K

RevealLayer Open Dataset

RevealLayer Open is the open-source dataset accompanying RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition.

Paper: https://arxiv.org/html/2605.11818v1 Accepted by ICML 2026

RevealLayer studies box-guided layered image decomposition for natural images. Given an RGB image and instance bounding boxes, the task is to decompose the scene into a clean background and object-level foreground layers, where each foreground layer is represented as RGBA.

This repository only redistributes data and annotations that are released by the RevealLayer authors. Third-party benchmark images and ground-truth annotations from AIM-500, RefMatte_RW100, and OBER-Test/ObjectClear are not included in this repository.

License

The RevealLayer dataset, processed annotations, metadata, and scripts released in this repository are licensed under the Apache License 2.0.

Some evaluation metadata or conversion scripts may refer to third-party benchmarks, including AIM-500, RefMatte_RW100, and OBER-Test/ObjectClear. Their original images and ground-truth annotations are not redistributed here. Users should download those datasets from their official sources and follow the corresponding original licenses and usage terms.

Repository Structure

A typical directory structure is:

RevealLayer_open/
├── train/
│   ├── <sample_id>/
│   │   ├── full_image.png
│   │   ├── background.png
│   │   ├── layer_0.png
│   │   ├── layer_1.png
│   │   └── ...
│   └── metaData.json
│
├── Benchmark/
│   ├── RevealLayerBenchMark-200/
│   │   ├── <sample_id>/
│   │   │   ├── full_image.png
│   │   │   ├── background.png
│   │   │   ├── layer_0.png
│   │   │   └── ...
│   │   └── metaData.json
│   │
│   └── RevealLayerBenchMark-wild/
│       ├── <sample_id>/
│       │   └── full_image.png
│       └── metaData.json
│
└── README.md

The exact number of layers varies across samples.

Metadata Format

Each split or benchmark subset contains a metaData.json file. It is a list of sample dictionaries.

Training / Fully Annotated Samples

A fully annotated sample generally follows this format:

{
  "imgid": "sample_id",
  "full_image": "sample_id/full_image.png",
  "background": "sample_id/background.png",
  "LayerInfoRaw": [
    "sample_id/layer_0.png",
    "sample_id/layer_1.png"
  ],
  "detections": [
    {
      "bbox": [x1, y1, x2, y2]
    }
  ]
}

Field meanings:

Field	Type	Description
imgid	string	Unique sample identifier.
full_image	string	Relative path to the original RGB image.
background	string	Relative path to the clean background image.
LayerInfoRaw	list[string]	Relative paths to object-level foreground RGBA layers.
detections	list[dict]	Instance bounding boxes used as box guidance.
bbox	list[number]	Bounding box in [x1, y1, x2, y2] format.

The detections field only keeps bounding boxes. Labels and confidence scores are not required for the RevealLayer task and are not included.

Wild Benchmark Samples

RevealLayerBenchMark-wild contains in-the-wild images with bounding-box annotations only. It does not include clean background ground truth or foreground RGBA ground truth.

A wild sample generally follows this format:

{
  "imgid": "sample_id",
  "full_image": "sample_id/full_image.png",
  "background": "",
  "LayerInfoRaw": [],
  "detections": [
    {
      "bbox": [x1, y1, x2, y2]
    }
  ]
}

For RevealLayerBenchMark-wild, the background field may be an empty string and LayerInfoRaw may be empty. This indicates that no background or foreground-layer ground truth is provided.

Benchmark Notes

Included Benchmark Subsets

• RevealLayerBenchMark-200: a fully annotated benchmark subset for evaluating background reconstruction and foreground RGBA layer decomposition.
• RevealLayerBenchMark-wild: a wild-image benchmark subset with full_image and bounding boxes only. It is intended for qualitative and real-world robustness evaluation. It does not contain background or foreground-layer ground truth.

Loading Example

import json
from pathlib import Path
from PIL import Image

root = Path("RevealLayer_open/train")
metadata_path = root / "metaData.json"

with open(metadata_path, "r", encoding="utf-8") as f:
    samples = json.load(f)

sample = samples[0]
full_image = Image.open(root / sample["full_image"]).convert("RGB")

background = None
if sample.get("background"):
    background = Image.open(root / sample["background"]).convert("RGB")

layers = []
for layer_path in sample.get("LayerInfoRaw", []):
    layers.append(Image.open(root / layer_path).convert("RGBA"))

boxes = [det["bbox"] for det in sample.get("detections", [])]

Data Usage Notes

• Paths in metaData.json are relative to the corresponding split or subset directory.
• Bounding boxes use [x1, y1, x2, y2] coordinates.
• Foreground layers are stored as RGBA images when ground truth is available.
• Some benchmark samples, especially wild images, may not contain background or foreground-layer ground truth.
• Third-party benchmark data are not redistributed in this repository. Users are responsible for complying with the original licenses when reproducing evaluations on those datasets.

Citation

If you find this dataset useful, please cite:

@inproceedings{wang2026reveallayer,
  title={RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition},
  author={Wang, Binhao and Zhao, Shihao and Cheng, Bo and Ji, Qiuyu and Ma, Yuhang and Wu, Liebucha and Liu, Shanyuan and Leng, Dawei and Yin, Yuhui},
  booktitle={International Conference on Machine Learning},
  year={2026}
}