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  "@type": "sc:Dataset",
  "name": "SysCON3D",
  "alternateName": [
    "syscon3d",
    "syscon3d-neurips26/syscon3d"
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  "description": "SysCON3D is a deterministic benchmark bundle for stress-testing multi-view 3D reconstruction backbones and 3D consistency metrics. It contains Mip-NeRF 360 reference images, calibration split manifests, and materialized inconsistent image sets including cross-scene mixtures, one-outlier samples, identical-image samples, Gaussian noise, patched Gaussian corruptions, and small Gaussian perturbations of otherwise consistent views.",
  "url": "https://huggingface.co/datasets/syscon3d-neurips26/syscon3d",
  "license": "https://huggingface.co/datasets/syscon3d-neurips26/syscon3d#license-and-source-data",
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  "version": "6",
  "citeAs": "SysCON3D anonymous NeurIPS submission, 2026.",
  "datePublished": "2026-05-07",
  "creator": {
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    "name": "Anonymous authors"
  },
  "keywords": [
    "3d reconstruction",
    "multi-view consistency",
    "benchmark",
    "Mip-NeRF 360",
    "robustness",
    "Croissant"
  ],
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      "@id": "readme",
      "name": "README.md",
      "description": "Dataset card with usage, extraction, source-data, and license notes.",
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      "@id": "impossible_manifest",
      "name": "mipnerf360_impossible_splits.json",
      "description": "SysCON3D stress-test split manifest with deterministic sample ids and image paths.",
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      "@id": "mipnerf360_archive_000",
      "name": "archives/syscon3d_mipnerf360_000.tar",
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    {
      "@type": "cr:FileSet",
      "@id": "materialized_syscon3d_images",
      "name": "Materialized SysCON3D stress-test images",
      "description": "Deterministic 224x224 PNG images for the materialized inconsistent scene types.",
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        "@id": "mipnerf360_archive_000"
      },
      "includes": "mipnerf360/syscon3d_scene_types/**/*.png",
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      "@id": "referenced_mipnerf360_images",
      "name": "Referenced Mip-NeRF 360 images",
      "description": "Referenced source images needed by the calibration splits and portable manifests.",
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      "@id": "camera_metadata",
      "name": "Camera metadata",
      "description": "Per-scene transform metadata for the referenced Mip-NeRF 360 scenes.",
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      "@id": "syscon3d_stress_test_samples",
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          "@id": "stress_test/sample_id",
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          "@id": "stress_test/subset_size",
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  "rai:dataLimitations": [
    "SysCON3D is designed for stress-testing multi-view 3D reconstruction backbones and 3D consistency metrics. It is not intended as a general-purpose training dataset, semantic recognition benchmark, or substitute for real deployment evaluation.",
    "Coverage is limited to nine static Mip-NeRF 360 scenes, deterministic image corruptions, fixed view counts, and 224x224 materialized stress-test images. Results may not generalize to dynamic scenes, human-centered scenes, outdoor-only or indoor-only deployment domains, or non-photographic imagery."
  ],
  "rai:dataBiases": [
    "The source scenes inherit the selection biases of Mip-NeRF 360, including a small number of mostly static real-world scenes and specific camera trajectories.",
    "The inconsistent samples intentionally over-represent synthetic and adversarial stress cases such as cross-scene mixtures and Gaussian corruptions; these samples are not representative of naturally occurring multi-view captures."
  ],
  "rai:personalSensitiveInformation": "The benchmark is based on public scene photographs and does not intentionally collect personal or sensitive attributes. It may still contain incidental real-world background content inherited from the source images.",
  "rai:dataUseCases": [
    "Recommended: evaluating robustness and abstention behavior of multi-view 3D reconstruction backbones and 3D consistency metrics under controlled stress tests.",
    "Not recommended: training production models, evaluating demographic fairness, evaluating semantic recognition, or making claims about safety outside the documented stress-test setting."
  ],
  "rai:dataSocialImpact": "The benchmark can improve transparency around failure modes of learned 3D reconstruction backbones and metrics. Misuse risk includes overclaiming robustness beyond the documented scenes and perturbations or treating synthetic stress-test behavior as equivalent to real-world safety.",
  "rai:hasSyntheticData": true,
  "rai:dataCollection": "Source photographs and camera metadata come from the Mip-NeRF 360 benchmark. SysCON3D selects referenced images and materializes deterministic stress-test samples from those sources plus synthetic image corruptions.",
  "rai:dataPreprocessingProtocol": "The release uses referenced-only packaging, rewrites manifests to portable paths under mipnerf360/, and stores materialized stress-test PNGs at 224x224. Synthetic scene types are generated deterministically from recorded sample ids, seeds, source paths, and corruption parameters in mipnerf360_impossible_splits.json.",
  "rai:dataAnnotationProtocol": "No human semantic labels are included. The manifests provide programmatic sample metadata such as sample id, scene type, view count, source scenes, source image paths, synthetic seeds, and corruption parameters.",
  "rai:dataReleaseMaintenancePlan": "The anonymous review release is versioned by the manifest field version=6 and by the Hugging Face dataset commit. Future updates should increment the manifest version and preserve prior release artifacts when possible.",
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      "@id": "https://jonbarron.info/mipnerf360/",
      "name": "Mip-NeRF 360"
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