| | --- |
| | license: cc-by-nc-4.0 |
| | task_categories: |
| | - depth-estimation |
| | tags: |
| | - stereo-matching |
| | - disparity |
| | - stereo4d |
| | - foundationstereo |
| | pretty_name: FFS Stereo4D |
| | size_categories: |
| | - 100K<n<1M |
| | --- |
| | |
| | # FFS Stereo4D |
| |
|
| | 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}, |
| | } |
| | ``` |
| |
|
