{
  "source_repository": "qherau/avoptim-benchmark",
  "source_commit": "73d5fd6e5b63a66fe4268f56a215d844a7516705",
  "source_directory": "reference/current",
  "source_hashes": {
    "README.md": "eefc1c78236b982421559441044428034d81fec3815cc7c0db6468bdf067ec57",
    "per_sequence.csv": "f259048908c20ca3d2103865881b05898eb2f92508f0849f3c96c88674abf2f2",
    "KITTI-360_pose.csv": "79042ff219d420327d9f6e79fda76a75018a29ada60132f2676d723a6db8c0e3",
    "NuScenes_pose.csv": "9b2031997d4b994c183a39f81668e3bc4d72357a730ee654e04e5e954db507fa",
    "PandaSet_pose.csv": "c679126dc566d21de8a1dcf588aeca57abfa9b2e5a9e93ebc643c9984d5040a6",
    "Waymo_pose.csv": "abe5aa25ff3f35c3b67e49b9e6213f772fc00640b16d1573a34f6001fb354c2b"
  },
  "aggregation": "Arithmetic mean of each metric over the same five slots per dataset. Overall is the arithmetic mean over all twenty slots, using unrounded per_sequence.csv values. No composite score.",
  "optimized_mode": "Extrinsic calibration + trajectory refinement",
  "baseline": "Original dataset poses",
  "metrics": [
    "nerfacto_psnr_db",
    "nerfacto_ssim",
    "nerfacto_lpips",
    "splatfacto_psnr_db",
    "splatfacto_ssim",
    "splatfacto_lpips",
    "reprojection_px",
    "track_length",
    "p2m_vertex_precision_fraction",
    "surface_distance_m"
  ],
  "rows": [
    {
      "dataset": "KITTI-360",
      "method": "Original",
      "sequences": 5,
      "metrics": [
        21.3,
        0.718,
        0.278,
        23.87,
        0.793,
        0.178,
        0.575,
        6.8,
        0.77,
        0.1
      ]
    },
    {
      "dataset": "KITTI-360",
      "method": "MOISST++",
      "sequences": 5,
      "metrics": [
        21.41,
        0.726,
        0.272,
        23.91,
        0.794,
        0.177,
        0.58,
        6.82,
        0.781,
        0.085
      ]
    },
    {
      "dataset": "NuScenes",
      "method": "Original",
      "sequences": 5,
      "metrics": [
        20.72,
        0.708,
        0.531,
        21.88,
        0.747,
        0.404,
        1.524,
        6.13,
        0.456,
        0.352
      ]
    },
    {
      "dataset": "NuScenes",
      "method": "MOISST++",
      "sequences": 5,
      "metrics": [
        22.26,
        0.794,
        0.376,
        26.44,
        0.859,
        0.242,
        0.948,
        8.54,
        0.733,
        0.1
      ]
    },
    {
      "dataset": "PandaSet",
      "method": "Original",
      "sequences": 5,
      "metrics": [
        21.91,
        0.699,
        0.52,
        23.54,
        0.774,
        0.318,
        1.164,
        5.53,
        0.505,
        0.311
      ]
    },
    {
      "dataset": "PandaSet",
      "method": "MOISST++",
      "sequences": 5,
      "metrics": [
        22.67,
        0.738,
        0.453,
        24.73,
        0.818,
        0.278,
        0.949,
        5.96,
        0.54,
        0.272
      ]
    },
    {
      "dataset": "Waymo",
      "method": "Original",
      "sequences": 5,
      "metrics": [
        23.28,
        0.756,
        0.493,
        23.13,
        0.775,
        0.432,
        1.168,
        5.98,
        0.467,
        0.42
      ]
    },
    {
      "dataset": "Waymo",
      "method": "MOISST++",
      "sequences": 5,
      "metrics": [
        24.4,
        0.781,
        0.447,
        25.02,
        0.809,
        0.367,
        1.065,
        6.46,
        0.695,
        0.357
      ]
    },
    {
      "dataset": "Overall",
      "method": "Original",
      "sequences": 20,
      "metrics": [
        21.802289962768555,
        0.7203991323709488,
        0.45537507385015485,
        23.105760669708253,
        0.7722669661045074,
        0.3329875349998474,
        1.1077661,
        6.10970425,
        0.5498052864225168,
        0.2959327582269907
      ]
    },
    {
      "dataset": "Overall",
      "method": "MOISST++",
      "sequences": 20,
      "metrics": [
        22.68612289428711,
        0.7598574370145798,
        0.3868135705590248,
        25.028763103485108,
        0.8199190974235535,
        0.2660898007452488,
        0.8856654,
        6.9459643,
        0.6873898219781452,
        0.20365932397544384
      ]
    }
  ],
  "metric_columns": [
    "Nerfacto PSNR",
    "Nerfacto SSIM",
    "Nerfacto LPIPS",
    "Splatfacto PSNR",
    "Splatfacto SSIM",
    "Splatfacto LPIPS",
    "Reproj. Error",
    "Track length",
    "P2M Prec.",
    "P2M average"
  ],
  "source_commit_scope": "Recorded reference metric CSVs; README.md is the current documentation snapshot."
}