| # From source meshes to the nine point cloud representations |
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| The optional `meshes/` archives preserve all 50,531 prepared OBJ files, paired with the point clouds by original partition, class and model ID. They restore `MCB_merged/train/<class>/<id>.obj` and `MCB_merged/test/<class>/<id>.obj`. The meshes are already normalized; they are the prepared conversion inputs, not the unprocessed upstream A/B download. `metadata/mesh_file_manifest.csv.gz` records every original OBJ SHA-256. |
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| The complete experiment repository is https://github.com/13536309143/MCB-32-Industrial-Point-Cloud-Classification . Its `data/convert_mcb_9sets.bat` and `data/_pointcloud_converter/` are also mirrored in this `conversion/` directory. The conversion software license is provided in `conversion/LICENSE`; it applies to the software, not the dataset. Source CAD rights remain subject to the notices at the dataset root. |
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| Download meshes with `python scripts/download_dataset.py --repo-id GeorgeWJJ/PointNext --meshes-only --output data` from a clone of the experiment repository. To download both point clouds and meshes, use `--include-meshes` instead. Add `--revision <dataset-commit>` for reproducibility. |
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| On Windows x64, arrange these entries beside each other, then double-click the BAT: |
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| ```text |
| data/ |
| MCB_merged/train/... |
| MCB_merged/test/... |
| convert_mcb_9sets.bat |
| _pointcloud_converter/ |
| bootstrap.ps1 |
| convert_mcb.py |
| sampling.cpp |
| sampling_windows_x64.dll |
| ``` |
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| The BAT prepares a local Python environment, checks prerequisites and calls the converter. The script writes `MCB_pointcloud/<method>_<count>/<train-or-test>/<class>/<id>.npy`, with area random, FPS and Poisson-disk-style sampling at 2,048/4,096/8,192 points, XYZ float32. It supports resumable checkpoints and does not change the source OBJ files. For converter options, run the Python converter with `--help`; the double-click BAT uses the default full conversion. The Windows wrapper is prepared but has not been validated on Windows hardware in the reported experiment. |
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| The C++ sampling source is provided alongside the Windows DLL. On macOS/Linux, build the native library and specify it explicitly, for example: |
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| ```bash |
| python -m pip install numpy==2.5.4 |
| # Linux; on macOS replace g++ with clang++ and -shared with -dynamiclib. |
| g++ -O3 -std=c++17 -shared -fPIC -ffp-contract=off data/_pointcloud_converter/sampling.cpp -o data/_pointcloud_converter/libsampling.so |
| python data/_pointcloud_converter/convert_mcb.py --source data/MCB_merged --output data/MCB_pointcloud --library data/_pointcloud_converter/libsampling.so --limit 1 |
| # Remove --limit 1 to convert the full collection after checking the small run. |
| ``` |
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| The exact protocol, fixed seed streams, class counts and geometry checks are described in `README.zh-CN.md` and the repository's `data/README.md`. Cross-platform floating-point/compiler differences can affect exact sampled coordinates. Download the published NPYs when byte identity with the completed experiment is required. |
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