| --- |
| license: cc-by-nc-4.0 |
| size_categories: |
| - 1M<n<10M |
| task_categories: |
| - depth-estimation |
| pretty_name: FFS Stereo4D |
| tags: |
| - stereo-matching |
| - disparity |
| - stereo4d |
| - foundationstereo |
| --- |
| |
| # FFS Stereo4D |
|
|
| [[Project Page]](https://nvlabs.github.io/Fast-FoundationStereo/) [[Paper]](https://huggingface.co/papers/2512.11130) [[Code]](https://github.com/NVlabs/Fast-FoundationStereo) |
|
|
| Disparity maps for stereo matching, generated from the [Stereo4D](https://github.com/niconielsen32/Stereo4D) dataset using [FoundationStereo](https://github.com/NVlabs/FoundationStereo). |
|
|
| ## Dataset Structure |
|
|
| ``` |
| data/train/ |
| metadata.csv |
| 0000000.zip (first 50,000 images) |
| 0000001.zip (next 50,000 images) |
| ... |
| 0000025.zip |
| ``` |
|
|
| Each zip contains disparity PNG files named `{vid_id}_frame_{frame_idx:06d}.png`. |
|
|
| - **Disparity images**: 3-channel uint8 784×784 PNG files encoding per-pixel disparity. Decode with: `disp = (R * 255*255 + G * 255 + B) / 1000.0`. See also: https://github.com/NVlabs/FoundationStereo/blob/master/scripts/vis_dataset.py |
| - **metadata.csv**: Links each disparity image back to its source YouTube video, with a `zip_file` column indicating which zip contains the image. |
|
|
| ### Metadata Columns |
|
|
| | Column | Description | |
| |---|---| |
| | `file_name` | Disparity image filename (inside the zip) | |
| | `zip_file` | Which zip file contains this image | |
| | `vid_id` | Clip identifier (matches the `.npz` calibration file) | |
| | `frame_idx` | Frame index in the rectified stereo output | |
| | `youtube_video_id` | YouTube video ID of the source 360 video | |
| | `timestamp_us` | Timestamp in microseconds in the original video | |
| | `timestamp_sec` | Timestamp in seconds | |
| | `video_frame_index` | Estimated frame number in the original video | |
| | `fps` | FPS of the source video | |
|
|
| ## Retrieving Source RGB Frames |
|
|
| This dataset contains **disparity maps only**. Due to the copyrights of these videos, users need to download on your own behalf. The corresponding left/right RGB stereo pairs can be recovered by: |
|
|
| 1. Following [stereo4d toolkit](https://github.com/Stereo4d/stereo4d-code) to download the YouTube video using `youtube_video_id`. |
| 2. Seek to `timestamp_sec` (or `video_frame_index`) to locate the source frame. |
| 3. Apply equirectangular rectification using the Stereo4D calibration `.npz` files to obtain the left and right perspective images. |
|
|
| ## Generation Pipeline |
|
|
| 1. **Source**: YouTube 360 videos from the Stereo4D dataset. |
| 2. **Rectification**: Equirectangular frames are rectified and cropped to 1024×1024 perspective stereo pairs. |
| 3. **Disparity estimation**: FoundationStereo computes dense disparity at 784×784 resolution (resized by `scale=0.765625` of the 1024×1024 input). |
|
|
| ### Camera Parameters |
|
|
| The rectified stereo pairs are generated at 1024×1024 with the following pinhole camera model: |
|
|
| | Parameter | Value (1024×1024 rectified) | Value (784×784 disparity) | Formula | |
| |---|---|---|---| |
| | HFOV | 60° | 60° | `output_hfov` in `batch_rectify.py` | |
| | Baseline | 0.063 m | 0.063 m | Assumed interpupillary distance for VR180 cameras | |
| | fx, fy | 886.8 px | 678.8 px | `size * 0.5 / tan(0.5 * HFOV * pi/180)` | |
| | cx, cy | 512 px | 392 px | Image center | |
|
|
| Depth is derived as: `depth = fx * baseline / disparity`. |
|
|
| Since disparity is computed at 784×784 resolution (scale factor 784/1024 = 0.765625 of the 1024×1024 input), use the 784×784 camera parameters when converting disparity to depth: |
|
|
| ```python |
| import numpy as np |
| hfov = 60 # degrees |
| baseline = 0.063 # meters |
| imw = 784 |
| fx = imw * 0.5 / np.tan(0.5 * np.radians(hfov)) # 678.8 px |
| depth = fx * baseline / disparity |
| ``` |
|
|
|
|
| ## Citation |
|
|
| If you use this dataset, please consider cite: |
|
|
| ```bibtex |
| @article{wen2026fastfoundationstereo, |
| title={Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching}, |
| author={Bowen Wen and Shaurya Dewan and Stan Birchfield}, |
| journal={CVPR}, |
| year={2026} |
| } |
| @article{wen2025foundationstereo, |
| title={FoundationStereo: Zero-Shot Stereo Matching}, |
| author={Wen, Bowen and Trepte, Matthew and Aribido, Joseph and Kautz, Jan and Birchfield, Stan and Wan, Yao}, |
| journal={CVPR}, |
| year={2025} |
| } |
| @inproceedings{jin2025stereo4d, |
| title={{Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos}}, |
| author={Jin, Linyi and Tucker, Richard and Li, Zhengqi and Fouhey, David and Snavely, Noah and Holynski, Aleksander}, |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
| year={2025}, |
| } |
| ``` |
