{
  "schema_version": 1,
  "generated_on": "2026-09-02",
  "purpose": "Project-page qualitative spread generated with the cleaned LVSPM release code.",
  "release_commit": "cbadad0a073366b144ed18f3c2fe2f9a77d5c707",
  "checkpoint": {
    "name": "lvspm_nvs_step5000.ckpt",
    "sha256": "5d465e456b12808b84eb87018890ffc0a2a32858af42b524415015ad3a2ccc5d",
    "missing_keys": [],
    "unexpected_keys": []
  },
  "evaluation": {
    "dataset": "DL3DV",
    "protocol": "equal-temporal",
    "context_views": 64,
    "held_out_target_views": 32,
    "canonical_index_sha256": "83a7acb4bf35cf1c90331b236152b66c79c8eeecf3e5b94fb0b70b2dfdda01b4",
    "three_scene_subset_sha256": "82daf81eebc5cdb0dfa843cc2f42e6a48ceadfbd83d64cc8ed30385cc7ee6ed7",
    "runtime_seconds_for_three_scenes": 36,
    "saved_image_counts_per_scene": {
      "input": 64,
      "ground_truth": 32,
      "prediction": 32
    }
  },
  "cases": [
    {
      "label": "strong",
      "selection": "rank 123 of 135 by stored scene PSNR (approximately 91st percentile)",
      "scene": "389a460ca1995e0658e85fe8e6b520b4e88b370cd6710dfe728b1564bba31aee",
      "metrics": {"psnr": 27.506269454956055, "ssim": 0.8700224161148071, "lpips": 0.14666211605072021},
      "comparison_frame": {"anysplat_target_order_index": 2, "depthsplat_dataset_frame_index": 13},
      "stored_metric_delta": {"psnr": 0.0037097930908203125, "ssim": 0.00003838539123535156, "lpips": 0.00005424022674560547}
    },
    {
      "label": "median",
      "selection": "rank 68 of 135 by stored scene PSNR (exact median)",
      "scene": "444da1b4a3f1ca019d4c9c1b405a579427746ccc96cab6dab7dcba5cb4fc998c",
      "metrics": {"psnr": 24.192039489746094, "ssim": 0.8354102969169617, "lpips": 0.13872835040092468},
      "comparison_frame": {"anysplat_target_order_index": 15, "depthsplat_dataset_frame_index": 50},
      "stored_metric_delta": {"psnr": 0.012186050415039062, "ssim": 0.00033295154571533203, "lpips": -0.00024105608463287354}
    },
    {
      "label": "hard",
      "selection": "rank 1 of 135 by stored scene PSNR (lowest)",
      "scene": "073f5a9b983ced6fb28b23051260558b165f328a16b2d33fe20585b7ee4ad561",
      "metrics": {"psnr": 19.32012939453125, "ssim": 0.35470104217529297, "lpips": 0.3924381136894226},
      "comparison_frame": {"anysplat_target_order_index": 15, "depthsplat_dataset_frame_index": 47},
      "stored_metric_delta": {"psnr": -0.0052051544189453125, "ssim": -0.00011730194091796875, "lpips": -0.00010389089584350586}
    }
  ],
  "video_comparisons": {
    "generated_on": "2026-09-02",
    "scenes": {
      "historic_car": {
        "scene": "dac9796dd69e1c25277e29d7e30af4f21e3b575d62a0a208c2b3d1211e2d5d77",
        "context_views": 64,
        "held_out_path_cameras": 18
      },
      "library": {
        "scene": "2cbfe28643b6636f9c70813cae7625aa858a352109493ac70fb429ce94dd55b3",
        "context_views": 64,
        "held_out_path_cameras": 12
      }
    },
    "path_protocol": "For every method, sort the canonical held-out target indices, then apply the LVSPM cubic translation and RotationSpline interpolation rule. LVSPM and DepthSplat render this path in their ground-truth-normalized frames. AnySplat receives no input camera poses; a single least-squares Sim(3), fitted from ground-truth context-camera centers to AnySplat's predicted context-camera centers, expresses the identical physical target path in AnySplat's reconstructed Gaussian frame.",
    "pose_inputs": {
      "LVSPM": "none",
      "AnySplat": "none",
      "DepthSplat": "ground-truth context poses"
    },
    "selection": "Selected at scene level after reviewing complete round-trip videos; no individual output frame was selected or removed."
  },
  "comparison_note": "LVSPM images were generated by the cleaned-code rerun. Static AnySplat and DepthSplat figures are pixel-matched saved outputs. The motion comparisons were freshly rendered for all three methods with the shared-path protocol above."
}