Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,234 @@
+---
+pretty_name: "MD-3K: MultiDepth-3K Benchmark"
+language:
+- en
+license: other
+size_categories:
+- 1K<n<10K
+task_categories:
+- depth-estimation
+tags:
+- depth-estimation
+- monocular-depth-estimation
+- ordinal-depth
+- spatial-reasoning
+- transparent-scenes
+- glass
+- multi-layer-depth
+- geometric-ambiguity
+- eccv-2026
+---
+
+# 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:
+
+```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="Xiaohao-Xu/MD-3K",  # 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}
+}
+```