DOPH: Differentiable Occupancy and Mesh Morphing

Differentiable Tool for Mesh2Occupancy & Mesh2SDF

This repository contains the code for the paper "Differentiable Voxelization and Mesh Morphing" by Yihao Luo et al. DOPH is a differentiable tool for mesh to occupancy and mesh to SDF conversion. Unlike previous tools implemented on the CPU used in Mesh2SDF and DOGN , DOPH is a differentiable tool that can be used to convert mesh to occupancy and SDF in a differentiable way, which can be directly integrated into the deep learning framework. Meanwhile, with GPU acceleration, DOPH can extract occupancy and SDF in arbitrary resolution in real-time, even for non-watertight (even for completely open meshes)，non-manifold, self-intersecting and combined meshes.

Installation

DOPH only depends on and . One can install the dependencies by running the following command:

pip install -r requirements.txt

Usage

Run the following command to convert a mesh to occupancy and SDF:

python mesh2voxel.py --mesh_path data_example/Bull.obj --mode occp --voxel_size 256 

The occupancy and SDF will be saved in the output folder, containing the following files:

output
├── Bull
│   ├── Bull_ori.obj
│   ├── Bull_occp_256.pt
│   ├── Bull_sdf_128.pt
│   └── Bull_occp_256.obj
...

where Bull_ori.obj is the original mesh, Bull_occp_256.pt is the occupancy in resolution 256, Bull_sdf_128.pt is the SDF in resolution 128, and Bull_occp_256.obj is the re-cubified mesh from the corresponding occupancy field.

Besides, we provide a tutorial demo_mesh2occp.ipynb to show the pipeline of DOPH to convert a mesh to occupancy and SDF. More generally, for a given mesh and query points, one can use the following code to get the occupancy and SDF:

import torch
from ops import occupancy, signed_distance_field, normalize_mesh
from pytorch3d.io import load_objs_as_meshes

# Load a mesh
mesh = load_objs_as_meshes("data_example/Bull.obj")
# normalize the mesh
mesh = normalize_mesh(mesh)

# Query the occupancy
q = torch.rand(100, 3)*2.-1.
q.requires_grad = True

# Get the occupancy
occp = occupancy(mesh, q)
print(occp)
# Get the SDF with gradient
sdf = signed_distance_field(mesh, q, allow_grad=True)
print(sdf)
print(sdf.grad)

The occupancy function is adaptive to non-watertight (almost-closed) meshes. For completely open meshes, we provide the flipped duplication method to convert the open mesh to a almost-closed mesh.

Citation

If you find this code useful for your research, please consider citing the following paper:

@article{lou2024doph,
    title={Differentiable Voxelization and Mesh Morphing},
    author={Yihao Luo, Yikai Wang,Zhengrui Xiang,
    Yuliang Xiu, Guang Yang, ChoonHwai Yap},
    journal={arXiv preprint arXiv:2407.11272},
    year={2024}}