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    "headline": "The LiDAR Learned Vision, Then Left the Camera Behind",
    "slug": "the-lidar-learned-vision-then-left-the-camera-behind",
    "dek": "A visual teacher trained cross-sensor point-cloud descriptors that ran camera-free at inference.",
    "summary": "A visual teacher trained cross-sensor point-cloud descriptors that ran camera-free at inference.",
    "body_text": "CVSD-Reg distills semantic structure from a frozen vision model into point-cloud representations, then adapts them for correspondence and pose estimation. One checkpoint reached strict registration success rates of 97.7, 99.0 and 99.3 percent on KITTI, nuScenes and HeLiPR, including 97.3 percent on sparse 16-beam scans. These are benchmark results; field robustness beyond those datasets remains unproven.",
    "why_it_matters": "A visual teacher trained cross-sensor point-cloud descriptors that ran camera-free at inference.",
    "limitations": [],
    "importance": 9,
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    "first_published_at": "2026-08-22T09:00:00.000-04:00",
    "modified_at": "2026-08-22T09:00:00.000-04:00",
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    "tags": [
      "LiDAR",
      "sensor transfer",
      "point clouds",
      "registration"
    ],
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  "sources": [
    {
      "source_id": "source-2026-08-22-010",
      "title": "arXiv preprint 2608.19536",
      "publisher": "arXiv",
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      "published_at": "2026-08-19T20:00:00.000-04:00",
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    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "The LiDAR Learned Vision, Then Left the Camera Behind",
    "publisher": "The Machine Press",
    "published_at": "2026-08-22T09:00:00.000-04:00",
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