--- pretty_name: "MD-3K: MultiDepth-3K Benchmark" language: - en license: other size_categories: - 1K **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: ```text MD-3K.zip ``` The expected SHA256 checksum is: ```text 3e9627d3aca6886a4449fd149b7ec791d13943d423fc5ee14f611c986bdecd29 MD-3K.zip ``` After extraction, the dataset should follow this layout: ```text 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`: ```python 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: ```python 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: ```text data/MD-3K/ ``` ## Integrity check After downloading, verify the archive. Linux: ```bash sha256sum -c checksums.txt ``` macOS: ```bash shasum -a 256 MD-3K.zip ``` Windows PowerShell: ```powershell Get-FileHash -Algorithm SHA256 MD-3K.zip ``` The expected SHA256 hash is: ```text 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: ```json { "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 ```text 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: ```text 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: ```bibtex @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: ```bibtex @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} } ```