| # Dataset Card | |
| **Number of samples**: 25196 | |
| **Columns / Features**: | |
| - **order_id**: Value(dtype='string', id=None) | |
| - **image_ids**: Sequence(feature=Value(dtype='string', id=None), length=-1, id=None) | |
| - **ade**: Sequence(feature=Image(mode=None, decode=True, id=None), length=-1, id=None) | |
| - **depth**: Sequence(feature=Image(mode=None, decode=True, id=None), length=-1, id=None) | |
| - **gestalt**: Sequence(feature=Image(mode=None, decode=True, id=None), length=-1, id=None) | |
| - **K**: Sequence(feature=Array2D(shape=(3, 3), dtype='float32', id=None), length=-1, id=None) | |
| - **R**: Sequence(feature=Array2D(shape=(3, 3), dtype='float32', id=None), length=-1, id=None) | |
| - **t**: Sequence(feature=Array2D(shape=(3, 1), dtype='float32', id=None), length=-1, id=None) | |
| - **wf_vertices**: Sequence(feature=Sequence(feature=Value(dtype='float32', id=None), length=3, id=None), length=-1, id=None) | |
| - **wf_edges**: Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=2, id=None), length=-1, id=None) | |
| - **wf_classifications**: Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None) | |
| - **colmap_binary**: Value(dtype='binary', id=None) | |
| These data were gathered over the course of several years throughout the United States from a variety of smart phone and camera platforms. | |
| Each training sample/scene consists of a set of posed image features (segmentation, depth, etc.) and a sparse point cloud as input, | |
| and a sparse wire frame (3D embedded graph) with semantically tagged edges as the target. | |
| In order to preserve privacy, original images are not provided. | |
| Note: the test distribution is not guaranteed to match the training set. | |
| # Important | |
| This dataset relies on the newest (0.2.111) version of the webdataset. Earlier version would not work, unfortunately. | |
| You can check all required dependencies in [requirements.txt](requirements.txt) | |
| ## Usage example | |
| ### Related package: hoho25k | |
| ```bash | |
| pip install git+http://hf.co/usm3d/tools2025.git | |
| ``` | |
| You can recreate the visualizations below with | |
| ```python | |
| from datasets import load_dataset | |
| from hoho2025.vis import plot_all_modalities | |
| from hoho2025.viz3d import * | |
| def read_colmap_rec(colmap_data): | |
| import pycolmap | |
| import tempfile,zipfile | |
| import io | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| with zipfile.ZipFile(io.BytesIO(colmap_data), "r") as zf: | |
| zf.extractall(tmpdir) # unpacks cameras.txt, images.txt, etc. to tmpdir | |
| # Now parse with pycolmap | |
| rec = pycolmap.Reconstruction(tmpdir) | |
| return rec | |
| ds = load_dataset("usm3d/hoho25k", streaming=True, trust_remote_code=True) | |
| for a in ds['train']: | |
| break | |
| fig, ax = plot_all_modalities(a) | |
| ## Now 3d | |
| fig3d = init_figure() | |
| plot_reconstruction(fig3d, read_colmap_rec(a['colmap_binary'])) | |
| plot_wireframe(fig3d, a['wf_vertices'], a['wf_edges'], a['wf_classifications']) | |
| plot_bpo_cameras_from_entry(fig3d, a) | |
| fig3d | |
| ``` | |
| ## Sample Preview | |
| ### Additional notes on data | |
| #### Depth | |
| The depth is a result of running monocular depth model [Metric3Dv2](https://huggingface.co/spaces/JUGGHM/Metric3D), | |
| and it is not ground truth by no means. The depth is stored in **millimeters**, so to get the metric data, use | |
| ```python | |
| np.array(entry['depth']).astype(float32)/1000. | |
| ``` | |
| If you need to have a GT depth, the semi-sparse depth from the Colmap reconstructions with dense features | |
| available in points3d is quite accurate. | |
| ### Segmentation | |
| You have two segmentations available. gestalt is domain specific model, which "sees-through-occlusions" and provides a detailed information about house parts. | |
| See the list of classes in "Dataset" section in the navigation bar. | |
| ade20k is a standard ADE20K segmentation model (specifically, [shi-labs/oneformer_ade20k_swin_large](https://huggingface.co/shi-labs/oneformer_ade20k_swin_large). | |
| ### Organizers | |
| Jack Langerman (Apple Inc), Dmytro Mishkin (Hover Inc / CTU in Prague), Yuzhong Huang (HOVER Inc)). | |
| ### Sponsors | |
| The organizers would like to thank Hover Inc. for their sponsorship of this challenge and dataset. | |
| ```bibtex | |
| @misc{S23DR_2025, | |
| title={S23DR Competition at 2nd Workshop on Urban Scene Modeling @ CVPR 2025}, | |
| url={usm3d.github.io}, | |
| howpublished = {\url{https://huggingface.co/usm3d}}, | |
| year={2025}, | |
| author={Langerman, Jack and Mishkin, Dmytro and Yuzhong, Huang} | |
| } | |
| ``` | |
| ## Sample Previews | |
| ### Sample 10 (row index 10) | |
| **2D Visualization**: All modalities | |
|  | |
| **3D Visualization**: Point cloud and wireframe | |
|  | |
| ### Sample 11 (row index 11) | |
| **2D Visualization**: All modalities | |
|  | |
| **3D Visualization**: Point cloud and wireframe | |
|  | |