This repository contains the code for using the H3DS dataset introduced in H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction

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Deep Learning python machine-learning deep-learning dataset
Overview

H3DS Dataset

PyPI

This repository contains the code for using the H3DS dataset introduced in H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction

Access

The H3DS dataset is only available for non-commercial research purposes. To request access, please fill in the contact form with your academic email. Your application will be reviewed and, after acceptance, you will recieve a H3DS_ACCESS_TOKEN together with the license and terms of use.

Setup

The simplest way to use the H3DS dataset is by installing it as a pip package:

pip install h3ds

Using H3DS

You can start using H3DS in your project with a few lines of code

from h3ds.dataset import H3DS

h3ds = H3DS(path='local/path/to/h3ds')
h3ds.download(token=H3DS_ACCESS_TOKEN)
mesh, images, masks, cameras = h3ds.load_scene(scene_id='1b2a8613401e42a8')

The returned types when loading a scene are Trimesh, list(PIL.Image) list(PIL.Image) and list(tuple(np.ndarray)).

To list the available scenes, simply use:

scenes = h3ds.scenes() # ['1b2a8613401e42a8', ...]

In order to reproduce the results from H3D-Net, we provide default views configurations for each scene:

views_configs = h3ds.default_views_configs(scene_id='1b2a8613401e42a8') # '3', '4', '8', '16' and '32'
mesh, images, masks, cameras = h3ds.load_scene(scene_id='1b2a8613401e42a8', views_config_id='3')

This will load a scene with 3 images, 3 masks and 3 cameras.

Please, see the provided examples for more insights.

Terms of use

By using the H3DS Dataset you agree with the following terms:

  1. The data must be used for non-commercial research and/or education purposes only.
  2. You agree not to copy, sell, trade, or exploit the data for any commercial purposes.
  3. If you will be publishing any work using this dataset, please cite the original paper.

Citation

@article{ramon2021h3d,
  title={H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction},
  author={Ramon, Eduard and Triginer, Gil and Escur, Janna and Pumarola, Albert and Garcia, Jaime and Giro-i-Nieto, Xavier and Moreno-Noguer, Francesc},
  journal={arXiv preprint arXiv:2107.12512},
  year={2021}
}
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Comments
  • Question about regions folder in each scene

    Question about regions folder in each scene

    Dear author,

    Thanks for providing datasets.

    While I am looking datasets, there is "regions" folder for each scene, containing 1) face.txt, 2) face_sphere.txt, 3) nose.txt.

    What are they for ? And what is the meaning of sequence of integers in each regions text file ?

    Thank you. Best regards, YJHong.

    opened by yjhong89 1
  • Evaluation doc

    Evaluation doc

    In this pull request we:

    • Clarify how to evaluate a new method.
    • Report the minor differences that we obtain when evaluating H3D-Net with this repo.
    • Provide our exact evaluation pipeline and the 3D reconstructions from the paper.
    opened by eduardramon 0
  • Evaluation

    Evaluation

    This PRs easies the evaluation process, which is now reduced to a single line of code:

    mesh_gt, images, masks, cameras = h3ds.load_scene(scene_id)
mesh_pred, landmarks_pred = your_reconstruction_method(images, masks, cameras)
chamfer_gt_pred, chamfer_pred_gt, mesh_gt, mesh_pred_aligned =  h3ds.evaluate_scene(scene_id, mesh_pred, landmarks_pred)

    The PR includes the following changes:

    • [x] The evaluation method evaluate_scene. It performs a coarse_alignment using the provided landmarks (optional), a fine alignment using ICP, and computes the chamfer distance (CD) in both directions. If a region_id is provided, the ICP is used only using this region and the CDs are computed only in this region as well.
    • [x]  Now meshes are handled with an internal Mesh class to avoid. This avoids the reordering in the vertices observed using Trimesh.
    • [x] Methods for aligning meshes (coarse and ICP) and for transforming them.
    • [x]  A method error_to_color to easily compute heatmaps.
    • [x]  An update of the examples/evaluation.py script.
    • [x]  An image showing the position of each landmark.
    • [x] An update of the README.md documentation.
    opened by eduardramon 0
  • H3ds v0.2.0

    H3ds v0.2.0

    This branch includes the following changes:

    • 13 more cases have been added. Now there are 23 in total.
    • Texture is now available for all the cases.
    • Scenes can be filtering by tags. The tag h3d-net can be used to reproduce the results of the paper.
    • Tests and docstrings for the principal class.
    • The version is currently checked to ensure the data is consistent with the code.
    • Minor improvements in the examples.
    opened by eduardramon 0
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Crisalix
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