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[Arxiv] Measuring the Discrepancy between 3D Geometric Models using Directional Distance Fields

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Measuring the Discrepancy between 3D Geometric Models using Directional Distance Fields

[Arxiv]

Installation

cd Closest_Point_on_Surface
python set_up.py install

Usage

Here we provide three versions of DirDist, i.e., Point-to-Point, Mesh-to-Mesh, and Mesh-to-Point (in the paper, it is face).

Optimize a point cloud according to the reference point cloud
from Closest_Point_on_Surface import DirDist_P2P

loss_func=DirDist_P2P()

......
loss=loss_func(src_points,tgt_points)
......
Optimize a triangle mesh according to the reference triangle mesh
from Closest_Point_on_Surface import DirDist_M2M

loss_func=DirDist_M2M()

......
loss=loss_func(src_v,src_f,tgt_v,tgt_f)
......
Optimize a triangle mesh according to the reference point cloud
from Closest_Point_on_Surface import DirDist_M2P

loss_func=DirDist_M2P()

......
loss=loss_func(src_v,src_f,tgt_points)
......

TODO

  • Release code.
  • Release examples on the selected applications
    • Shape Deformation
    • Rigid Registration
    • Non-rigid Registration
    • Scene Flow Estimation
    • Human Pose Optimization

Acknowledgement

We thank the following excellent works including MAC, AMM, NSFP, SCOOP, and MDA for their released codes.

Citation

@article{ren2024measuring,
  title={Measuring the Discrepancy between 3D Geometric Models using Directional Distance Fields},
  author={Ren, Siyu and Hou, Junhui and Chen, Xiaodong and Xiong, Hongkai and Wang, Wenping},
  journal={arXiv preprint arXiv:2401.09736},
  year={2024}
}

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[Arxiv] Measuring the Discrepancy between 3D Geometric Models using Directional Distance Fields

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