xiaohaox/MultiDepth-3K-Dataset

MD-3K: MultiDepth-3K Benchmark

MD-3K is a real-world diagnostic benchmark for probing geometric ambiguity in monocular depth estimation. It focuses on transparent scenes where a single camera ray can contain two visually present and geometrically valid surfaces: a transparent foreground surface and the visible background behind it. Since a standard monocular depth model returns one scalar depth per pixel, MD-3K evaluates which valid depth layer a model reports and whether paired hypotheses can jointly satisfy both layer-specific ordinal relations.

This dataset accompanies the ECCV 2026 paper:

One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models
Xiaohao Xu, Feng Xue, Xiang Li, Haowei Li, Shusheng Yang, Tianyi Zhang, Matthew Johnson-Roberson, Xiaonan Huang.

Code repository: https://github.com/Xiaohao-Xu/Ambiguity-in-Space

Release files

The canonical archive in this release is:

MD-3K.zip

The expected SHA256 checksum is:

3e9627d3aca6886a4449fd149b7ec791d13943d423fc5ee14f611c986bdecd29  MD-3K.zip

After extraction, the dataset should follow this layout:

MD-3K/
├── images/              # RGB images
├── masks/               # ambiguous/transparent-region masks
└── annotations.json     # sparse point-pair ordinal annotations

Dataset summary

MD-3K contains:

• 3,161 RGB images curated for transparent-scene ambiguity analysis.
• Ambiguous-region masks.
• Sparse point-pair annotations in ambiguous regions.
• Two layer-specific ordinal targets:
  • Layer 1: transparent foreground surface.
  • Layer 2: visible background behind the transparent surface.
• Same/Reverse subset structure:
  • Same: the two valid layers induce the same ordinal relation.
  • Reverse: the two valid layers induce conflicting ordinal relations.

The Reverse subset is the key diagnostic setting: a duplicated single-depth hypothesis cannot satisfy both valid layer relations.

Download

Download the archive with huggingface_hub:

from huggingface_hub import hf_hub_download

zip_path = hf_hub_download(
    repo_id="xiaohaox/MultiDepth-3K-Dataset",  # change this if the dataset is released under another HF namespace
    filename="MD-3K.zip",
    repo_type="dataset",
)
print(zip_path)

Extract locally:

from pathlib import Path
from zipfile import ZipFile

zip_path = Path(zip_path)
out_dir = Path("data")
out_dir.mkdir(parents=True, exist_ok=True)

with ZipFile(zip_path, "r") as zf:
    zf.extractall(out_dir)

print(f"Extracted to: {out_dir.resolve()}")

Expected local path for the GitHub evaluation scripts:

data/MD-3K/

Integrity check

After downloading, verify the archive.

Linux:

sha256sum -c checksums.txt

macOS:

shasum -a 256 MD-3K.zip

Windows PowerShell:

Get-FileHash -Algorithm SHA256 MD-3K.zip

The expected SHA256 hash is:

3E9627D3ACA6886A4449FD149B7EC791D13943D423FC5EE14F611C986BDECD29

Annotation schema

The canonical annotation file is annotations.json. It maps an image path to a list of sparse point-pair annotations. A typical record is:

{
  "images/example.jpg": [
    {
      "point1": [x1, y1],
      "point2": [x2, y2],
      "foreground_label": "near_or_far",
      "background_label": "near_or_far",
      "subset": "same_or_reverse"
    }
  ]
}

If a compact legacy schema is used, it may contain a single label field. In that case, follow the repository evaluation protocol and document the mapping explicitly. Do not describe label = 1 as SRA(1) or label = 2 as SRA(2): SRA(1) and SRA(2) are evaluation targets for the foreground and background layers, not merely dataset label values.

Coordinate convention: point coordinates are stored as [x, y]. When indexing an image or depth array with NumPy/OpenCV, use [y, x].

Depth convention

Before comparing a predicted depth map to MD-3K ordinal labels, verify the saved prediction convention.

Some models save depth-like outputs, where larger values indicate farther points. Other models save inverse-depth or disparity-like outputs, where larger values indicate nearer points. MD-3K evaluation requires a consistent near/far convention. If a prediction is inverse depth or disparity, convert it to depth, or equivalently flip the ordinal comparison before computing SRA(1), SRA(2), depth-layer preference, or ML-SRA.

Metrics

SRA(1)

Spatial Relationship Accuracy with respect to the transparent foreground layer.

SRA(2)

Spatial Relationship Accuracy with respect to the visible background layer.

Depth-layer preference

alpha = SRA(2) - SRA(1)

Positive alpha indicates stronger background-layer preference. Negative alpha indicates stronger foreground-layer preference.

ML-SRA

Multi-Layer Spatial Relationship Accuracy evaluates whether a paired output, such as RGB and LVP predictions, jointly satisfies both layer-specific ordinal relations.

In the paper protocol, RGB and LVP are assigned according to the model's RGB depth-layer preference at the benchmark level:

• If RGB prefers layer 1, assign RGB to layer 1 and LVP to layer 2.
• If RGB prefers layer 2, assign RGB to layer 2 and LVP to layer 1.

This is not a per-image oracle and should not be described as automatic test-time layer selection. Alternative pairing strategies may be useful future improvements, but they should be reported separately.

Associated code and LVP

The evaluation and analysis code is maintained in the GitHub repository:

https://github.com/Xiaohao-Xu/Ambiguity-in-Space

Laplacian Visual Prompting (LVP) is a deterministic, training-free input transformation. It has no learned parameters and no checkpoint, so it is not released here as a Hugging Face model. The intended use is to apply the transform to the input image, map the result back to a standard RGB image representation, and pass it through the same depth-model processor used for the original RGB input.

Recommended use

Use MD-3K to:

• characterize depth-layer preference in monocular depth models,
• compare RGB and LVP behavior,
• evaluate paired-hypothesis complementarity with ML-SRA,
• analyze Same and Reverse subsets separately.

Do not use MD-3K to claim dense metric multi-layer depth recovery unless additional dense ground truth and evaluation are provided.

Limitations

MD-3K is a focused diagnostic benchmark. It is not intended to exhaustively cover all transparency, reflection, refraction, material, or open-world 3D ambiguity cases. Its annotations are sparse ordinal point-pair labels rather than dense metric multi-layer depth maps. It should be used for controlled analysis of model behavior under layered transparent-scene ambiguity, not as a standalone guarantee of robotic safety or deployable open-world depth robustness.

License and data terms

This dataset card uses license: other because the release contains dataset annotations/masks and RGB imagery curated from upstream transparent/glass-scene data. Please follow the terms of this MD-3K release and the applicable upstream data terms. If you decide to publish the complete archive under a specific standard license, update both the YAML metadata and this section consistently.

Citation

If you use MD-3K, please cite:

@inproceedings{xu2026onescenetwodepths,
  title={One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models},
  author={Xu, Xiaohao and Xue, Feng and Li, Xiang and Li, Haowei and Yang, Shusheng and Zhang, Tianyi and Johnson-Roberson, Matthew and Huang, Xiaonan},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2026}
}

Also, the segmentation labels are aourced from GDD dataset. So please cite:

@inproceedings{Mei_2020_CVPR,
    author = {Mei, Haiyang and Yang, Xin and Wang, Yang and Liu, Yuanyuan and He, Shengfeng and Zhang, Qiang and Wei, Xiaopeng and Lau, Rynson W.H.},
    title = {Don't Hit Me! Glass Detection in Real-World Scenes},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2020}
}