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Running on Zero
Running on Zero
docs: link reproducible evaluation tools
Browse files
README.md
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@@ -43,6 +43,15 @@ Maintainers can reproduce the no-model hosted check with
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[`hosted_smoke_test.py`](hosted_smoke_test.py), and use
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[`TROUBLESHOOTING.md`](TROUBLESHOOTING.md) for failure-stage guidance.
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## Important limitations
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This Space does not claim metric-scale reconstruction, learned Gaussian
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[`hosted_smoke_test.py`](hosted_smoke_test.py), and use
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[`TROUBLESHOOTING.md`](TROUBLESHOOTING.md) for failure-stage guidance.
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For quantitative work, use [`evaluate_depth.py`](evaluate_depth.py) and
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[`evaluate_depth_dataset.py`](evaluate_depth_dataset.py) for depth arrays,
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[`evaluate_pointcloud.py`](evaluate_pointcloud.py) and
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[`evaluate_pointcloud_dataset.py`](evaluate_pointcloud_dataset.py) for exported
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3D/Gaussian positions, and validate calibrated inputs with
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[`validate_capture_manifest.py`](validate_capture_manifest.py). The copyable
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manifest format is documented in
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[`CAPTURE_MANIFEST_SCHEMA.md`](CAPTURE_MANIFEST_SCHEMA.md).
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## Important limitations
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This Space does not claim metric-scale reconstruction, learned Gaussian
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