docs(P6): reframe README around scaffold thesis and 5-arm results - #15
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Summary by BeetleThis PR reframes the project's README around the "Scaffold Thesis" — the principle that geometry scaffolds structure for free while deep learning earns its keep on objects. The documentation now presents comprehensive S3DIS Area-5 benchmark results across five distinct evaluation arms (geometry_only, hybrid, hybrid_v2, feature_ml, and PointNet++), demonstrating that rule-based geometry captures ~63.6% of structural points with high fidelity (floor IoU: 0.963), while deep learning (PointNet++: 0.3523 mIoU) proves necessary for complex furniture classification. The README transformation shifts from a simple "how it works" guide to a research-backed narrative with quantitative evidence, detailed metrics tables, and clear takeaways about the boundary between geometric and learned approaches. 📁 File Changes Summary
Total Changes: 1 file changed, +79 additions, -35 deletions 🎯 Key Changes
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Code Review
This pull request updates the README.md to introduce the Scaffold Thesis, include detailed S3DIS Area-5 benchmark results, and reorganize the pipeline architecture, quickstart, and documentation sections. The review feedback highlights two documentation inconsistencies: step 2 of the quickstart incorrectly describes running the 'Segment-ML Pipeline' instead of the rule-based geometric pipeline, and the documented output filenames are inconsistent with those listed in implementation.md.
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| ### 2. Run the Segment-ML Pipeline | ||
| Segment the room and predict using the feature-ML model: |
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The Quickstart section describes step 2 as running the 'Segment-ML Pipeline' and predicting using the 'feature-ML model'. However, main.py orchestrates the rule-based geometric pipeline (SemanticLabeler with heuristics) rather than the classical Random Forest model (feature_ml). Please update the description to accurately reflect that this command runs the rule-based geometric pipeline.
| ### 2. Run the Segment-ML Pipeline | |
| Segment the room and predict using the feature-ML model: | |
| ### 2. Run the Geometric Pipeline | |
| Segment the room and predict using rule-based geometric heuristics: |
| ```bash | ||
| python3 -m src.interactive_viewer --input outputs/segmented_room.ply | ||
| ``` | ||
| *Each run generates `segmented_room.ply` (labeled cloud), `segmentation_report.json` (validated cluster report), and `segmentation_viz.png` (2D top-down footprint map).* |
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There is an inconsistency in the output filenames documented across different files. Here, it states that the pipeline generates segmented_room.ply, segmentation_report.json, and segmentation_viz.png. However, implementation.md (lines 1013-1015) lists them as segmented.ply, report.json, and topdown_map.png. Please standardize these filenames across all documentation files to avoid confusion.
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This PR updates README.md to focus on the scaffold thesis and includes the final 5-arm S3DIS Area-5 benchmark results table.