| | |
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| | | currently displayed orientation. |
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| | | An incorrect revision can be removed after confirmation, but the endpoint only |
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| | | removes that revision directory and cannot remove LAS/PLY sources, semantic |
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| | | results, or training outputs. A local CPU training job requires two classes with |
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| | | at least 500 confirmed points each, then writes a portable model, metrics, and |
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| | | predicted PLY asynchronously. Sources, point indices, class codes, Python |
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| | | interpreter and command are all server-fixed; the same model pipeline can later |
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| | | use CUDA without data or code changes. |
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| | | results, or training outputs. A local training job requires two classes with at |
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| | | least 500 confirmed points each, then writes a portable model, metrics, and |
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| | | predicted PLY asynchronously. The server probes only the fixed |
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| | | `05-3d-pointcloud-gpu` interpreter; when CUDA is available it uses that |
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| | | environment, otherwise it uses the retained fixed CPU interpreter. Sources, |
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| | | point indices, class codes, Python interpreters and commands are all server-fixed. |
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| | | Each job reports the selected device, environment, and PyTorch version. |
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| | | |
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| | | The supervised area also exposes **Apply trained model**. It discovers only |
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| | | complete local training directories containing both `model.pt` and `metrics.json`. |
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| | | Select a discovered model, upload one new RGB PLY/PCD/LAS/LAZ point cloud, and |
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| | | the server copies its bytes into new `raw` and `processed` inference directories |
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| | | before starting a background CPU job. The API accepts neither a browser path nor |
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| | | before starting a background job that automatically selects the verified GPU or |
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| | | CPU environment. The API accepts neither a browser path nor |
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| | | an arbitrary model path. Completion shows the classified PLY preview directly in |
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| | | the workspace and provides downloads for classified LAS, class-count CSV, |
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| | | prediction JSON, metadata, and model weight. XYZ-only input fails explicitly: |
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| | |
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| | | 3. **Point-cloud semantic classification**: classification cases, coloured |
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| | | point preview, semantic LAS/CSV/GeoJSON and review-only rule outputs. |
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| | | 4. **Annotation, training, and model application**: RGB annotation source, |
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| | | annotation revisions, CPU training, training metrics, and applying a trained |
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| | | annotation revisions, automatic GPU/CPU training, training metrics, and applying a trained |
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| | | model to a new RGB cloud. |
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| | | |
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| | | Each workspace filters the case library to its own purpose. A point-cloud run |
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