| # Masked-autoencoder pretraining |
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| Separate QL1 DataParallel and QL2 DistributedDataParallel loops are ported |
| from the corresponding research repositories. Model, optimizer, masking, |
| dataset, and scheduler settings are in the YAMLs. Use a GPU allocation with |
| working PointNet++ and Chamfer extensions. |
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| Inputs are cropped NumPy XYZ arrays in feet. Edit each split's `data_root` |
| and `pattern` in a copied config; data are not distributed. Dataset code |
| samples/pads 20,480 points, applies configured vertical clipping, centers at |
| the sampled bounding-box midpoint, and divides XYZ by `[50,50,25]` (QL1) or |
| `[100,100,75]` (QL2). Training applies quarter-turn augmentation. |
| This differs from downstream normalization. |
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| QL2 stage 2 starts from stage 1's best checkpoint. Stage 1's original config |
| allowed 300 epochs, but the recorded run stopped around epoch 152; the config |
| maximum does not mean completed training. Final stage-2 best is epoch 80. |
| Re-running the full stage-1 maximum is a new run and need not reproduce that |
| lineage. `--max-epoch` sets a stop epoch while retaining the scheduler period. |
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| Use `--resume` with the same output directory for a trusted full training |
| checkpoint. Release weights omit optimizer state; use a full `mae` artifact |
| with `--start-checkpoint` for initialization, not exact resumption. |
| Encoder-only weights cannot initialize the reconstruction decoder strictly. |
| Resume preserves best-checkpoint selection, but inherited loops do not |
| restore all RNG/scaler state; bitwise resumed-run equivalence is not claimed. |
| Global batch size must be divisible by DDP world size. |
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| The release contains model training and downstream pipelines. It does not |
| contain private split manifests or every historical data-generation and |
| ablation script. Original split/crop/thinning provenance is necessary to |
| reproduce the paper's specific experiments. |
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