{"id":18792547,"url":"https://github.com/prbonn/vdb_to_numpy","last_synced_at":"2025-04-13T14:31:21.941Z","repository":{"id":64789558,"uuid":"480339860","full_name":"PRBonn/vdb_to_numpy","owner":"PRBonn","description":"Tool to convert VDB grids to numpy arrays.","archived":false,"fork":false,"pushed_at":"2023-01-02T06:37:34.000Z","size":391,"stargazers_count":4,"open_issues_count":0,"forks_count":3,"subscribers_count":4,"default_branch":"main","last_synced_at":"2023-03-17T10:30:54.399Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/PRBonn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null}},"created_at":"2022-04-11T10:55:22.000Z","updated_at":"2022-12-17T04:07:28.000Z","dependencies_parsed_at":"2023-02-01T02:01:11.507Z","dependency_job_id":null,"html_url":"https://github.com/PRBonn/vdb_to_numpy","commit_stats":null,"previous_names":[],"tags_count":null,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PRBonn%2Fvdb_to_numpy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PRBonn%2Fvdb_to_numpy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PRBonn%2Fvdb_to_numpy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PRBonn%2Fvdb_to_numpy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PRBonn","download_url":"https://codeload.github.com/PRBonn/vdb_to_numpy/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223589837,"owners_count":17170045,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-07T21:20:24.784Z","updated_at":"2024-11-07T21:20:25.484Z","avatar_url":"https://github.com/PRBonn.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# VDB to Numpy\n\nA python utility library to convert Triangular Meshes/VDB grids into numpy arrays. This project was\ncreated to work with [vdbfusion](https://github.com/PRBonn/vdbfusion)\n\n## Dependencies\n\n- OpenVDB, please check official documentation.\n- [manifold_python](https://github.com/PRBonn/manifold_python.git)\n- Check the [Dockerfile](./docker/builder/Dockerfile) for more details ...\n\n## Usage\n\n### With VDBFusion\n\nThe main idea of this `vdb_to_numpy` package was to use it with maps generated with\n[vdbfusion](https://github.com/PRBonn/vdbfusion). More specifically, to train some neural networks\nwith this type of data.\n\nTo use this package in such way, you first need some VDBs, to do so, go and checkout the [vdbfusion\nexamples](https://github.com/PRBonn/vdbfusion/tree/main/examples/python). This examples will spit\nsome VDB files, that you can right away use with some of the [apps](./apps) on this project. There\nare some other [experiments](./experiments) to checkout.\n\n### Mesh-to-sdf\n\nIf you only want to use this package to convert triangular meshes to SDF fields then you probably\nwant to use the `mesh_to_sdf` docker container to convert your meshes and then runaway. If you need\nextra funcionallity you can clone this repo and install the tool locally and start messing around\nwith the example [apps](./apps/). If not, this is the easiest entry point:\n\nFor doing so, just run this command, your current working directory will be\nmounted to the `/models/` path in the container.\n\n```sh\ndocker run -it --rm \\\n    -v $(pwd):/models \\\n    --user 1000:1000 \\\n    ignaciovizzo/vdb_to_numpy:mesh_to_sdf \\\n    /models/tests/test_data/bunny.ply \\\n    --scale \\\n    --watertight \\\n    --mcubes\n```\n\nThis command mounts your current working directory to the /models directory in\nthe docker container and executes the [mesh_to_sdf.py](apps/mesh_to_sdf.py)\nscript on the mesh file `bunny.ply` in the current directory.\n\nThe output of this command will be the following files:\n\n```sh\n├── tests\n│   ├── test_data\n│   │   ├── bunny.ply           # Input mesh, not watertigh, not to scale\n│   │   ├── bunny_sdf.npy       # Output numpy SDF dense grid\n│   │   ├── bunny_sdf_mesh.ply  # Output mesh, after running marching cubes\n```\n\nIf you need extra help just:\n\n```sh\ndocker run -it --rm ignaciovizzo/vdb_to_numpy:mesh_to_sdf --help\n```\n\n**NOTE:** I've created this repoisotry in 2021 and I'm currently not actively using it. The API is\n[tested](./tests) but use it at your own risk ;)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprbonn%2Fvdb_to_numpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprbonn%2Fvdb_to_numpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprbonn%2Fvdb_to_numpy/lists"}