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# HoHo 5k Subset

This dataset is being used as the training set for the [S23DR Challenge](https://huggingface.co/spaces/usm3d/S23DR).

This is a living dataset. Today, we provide 4316 samples for training, and 175 for validation and hold back an additional 1072 for computing the private and public leaderboards. Additional, we intend to continue releasing *training* data throughout the challenge and beyond. The data take the following form:


```python
Features({
        "order_id": Value(dtype="string"),
        # inputs 
        "K": Sequence(Array2D(dtype="float32", shape=(3, 3))),
        "R": Sequence(Array2D(dtype="float32", shape=(3, 3))),
        "t": Sequence(Sequence(Value(dtype="float32"), length=(3))), # in centimeters
        "gestalt": Sequence(Image()), 
        "ade20k": Sequence(Image()),  
        "depthcm": Sequence(Image()),  # in centimeters
        # result of Colmap reconstruction loaded in named tuples
        # More on format: https://github.com/colmap/colmap/blob/main/scripts/python/read_write_model.py#L47
        "images": Dict(namedtuple("Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"])),
        "points3d": Dict(namedtuple( "Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"])),
        "cameras": Dict(namedtuple("Camera", ["id", "model", "width", "height", "params"])),
 
        # side info during training
        "mesh_vertices": Sequence(Sequence(Value(dtype="float32"), length=3)),
        "mesh_faces": Sequence(Sequence(Value(dtype="int64"))),
        "face_semantics": Sequence(Value(dtype='int64')),
        "edge_semantics": Sequence(Value(dtype='int64')),

        # targets
        "wf_vertices": Sequence(Sequence(Value(dtype="float32"), length=3)),  # in centimeters
        "wf_edges": Sequence(Sequence(Value(dtype="int64"), length=2)),
   })
```
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. Additionally a mesh with semantically tagged faces is provided for each scene durning training. In order to preserve privacy, original images are not provided.

Note: the test distribution is not guaranteed to match the training set.

### Sample visualizations 

Two visualizations of houses below are interactive! Grab one with your mouse and rotate!
<div style='width: 100%;'>
    <div style='margin: auto;'>
        <iframe style="width: 45%; height:  512px; display: inline; margin: 0px; padding: 0px;" frameBorder="0" src="https://usm3d.github.io/S23DR/inputs_8d74b83100f.html">no iframe support</iframe>
        <iframe style="width: 45%; height:  512px; display: inline; margin: 0px; padding: 0px;" frameBorder="0" src="https://usm3d.github.io/S23DR/outputs_8d74b83100f.html">no iframe support</iframe>
    </div>
</div>
<img src="https://raw.githubusercontent.com/usm3d/usm3d.github.io/main/images/hf_hotlinks/input_images_8d74b83100f.webp" alt="Input Images" style="width: 100%;">


<img src="https://raw.githubusercontent.com/usm3d/usm3d.github.io/main/images/hf_hotlinks/order_vis1.png" alt="Order 1" style="width: 33%; display: inline;">
<img src="https://raw.githubusercontent.com/usm3d/usm3d.github.io/main/images/hf_hotlinks/order_vis2.png" alt="Order 2" style="width: 33%; display: inline;">
<img src="https://raw.githubusercontent.com/usm3d/usm3d.github.io/main/images/hf_hotlinks/order_vis3.png" alt="Order 3" style="width: 33%; display: inline;">
<img src="https://raw.githubusercontent.com/usm3d/usm3d.github.io/main/images/hf_hotlinks/images_vis.png" alt="Input IMage s" style="width: 100%;">
<img src="https://raw.githubusercontent.com/usm3d/usm3d.github.io/main/images/hf_hotlinks/roof_diagram.png" alt="Order 3" style="width: 100%;">

The roofs below are interactive as well!

<iframe style="width: 100%; height:  512px;" src="https://usm3d.github.io/S23DR/houses.html">no iframe support</iframe>


### Additional notes on data

#### Depth
The `depthcm` is a result of running monocular depth model, and it is not ground truth by no means. If you need to have a GT depth, you can render the GT mesh in the training set using `mesh_faces` and `mesh_vertices`. 
The semi-sparse depth from the Colmap reconstructions with dense features, available in `points3d` is much more accurate, than `depthcm`. 

At the inference time, `mesh_faces` is not available, so you can use only `depthcm` and colmap point cloud from `points3d`

#### 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 (Hover), Dmytro Mishkin (CTU in Prague / Hover), Ilke Demir (Intel), Hanzhi Chen (TUM), Daoyi Gao (TUM), Caner Korkmaz (ICL), Tolga Birdal (ICL)

## Sponsors 
The organizers would like to thank Hover Inc. for their sponsorship of this challenge and dataset.

## Timeline
- Competition Released: March 14, 2024
- Entry Deadline: May 28, 2024
- Team Merging: May 28, 2024
- Final Solution Submission: June 4, 2024
- Writeup Deadline: June 11, 2024

## Prizes
- 1st Place: **$10,000**
- 2nd Place: **$7,000**
- 3rd Place: **$5,000**
- Additional Prizes: **$3,000**

Please see the [Competition Rules](https://usm3d.github.io/S23DR/s23dr_rules.html) for additional information.


### Cite
```
@misc{Langerman_Korkmaz_Chen_Gao_Demir_Mishkin_Birdal2024, 
        title={S23DR Competition at 1st Workshop on Urban Scene Modeling @ CVPR 2024}, 
        url={usm3d.github.io},
        howpublished = {\url{https://huggingface.co/usm3d}},
        year={2024},
        author={Langerman, Jack and Korkmaz, Caner and Chen, Hanzhi and Gao, Daoyi and Demir, Ilke and Mishkin, Dmytro and Birdal, Tolga}
    } 
```