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Night3R-train

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
source_candidates: string
selection_notes: string
data_root: string
best_model_run_used_for_selection: string
best_model_checkpoint_used_for_selection: string
official_eval_protocol: struct<pair_sampling: string, pair_stride: int64, max_batches: int64, note: string>
  child 0, pair_sampling: string
  child 1, pair_stride: int64
  child 2, max_batches: int64
  child 3, note: string
visual_debug_protocol: struct<pair_sampling: string, pair_stride: int64, max_pairs_per_scene: int64, note: string>
  child 0, pair_sampling: string
  child 1, pair_stride: int64
  child 2, max_pairs_per_scene: int64
  child 3, note: string
selected: list<item: struct<scene: string, path: string, chunk_id: int64, score: double, brightness_mean: doub (... 239 chars omitted)
  child 0, item: struct<scene: string, path: string, chunk_id: int64, score: double, brightness_mean: double, contras (... 227 chars omitted)
      child 0, scene: string
      child 1, path: string
      child 2, chunk_id: int64
      child 3, score: double
      child 4, brightness_mean: double
      child 5, contrast_mean: double
      child 6, event_active_frac_mean: double
      child 7, event_abs_mean: double
      child 8, num_events_median_meta: double
      child 9, depth_valid_frac_mean: double
      child 10, depth_median_mean: double
      child 11, depth_p95_mean: double
      child 12, trans_0_7_m: double
      child 13, rot_0_7_deg: double
name: string
backup_old_eval_root: string
split_policy: string
related_split_manifest: string
created_on: timestamp[s]
source_root: string
excluded_from_test: list<item: struct<scene: string, sequence: string, reason: string>>
  child 0, item: struct<scene: string, sequence: string, reason: string>
      child 0, scene: string
      child 1, sequence: string
      child 2, reason: string
to
{'name': Value('string'), 'created_on': Value('timestamp[s]'), 'split_policy': Value('string'), 'selection_notes': Value('string'), 'data_root': Value('string'), 'source_root': Value('string'), 'backup_old_eval_root': Value('string'), 'related_split_manifest': Value('string'), 'official_eval_protocol': {'pair_sampling': Value('string'), 'pair_stride': Value('int64'), 'max_batches': Value('int64'), 'note': Value('string')}, 'visual_debug_protocol': {'pair_sampling': Value('string'), 'pair_stride': Value('int64'), 'max_pairs_per_scene': Value('int64'), 'note': Value('string')}, 'selected': List({'scene': Value('string'), 'path': Value('string'), 'sequence': Value('string'), 'frames': Value('int64'), 'split': Value('string')}), 'excluded_from_test': List({'scene': Value('string'), 'sequence': Value('string'), 'reason': Value('string')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              source_candidates: string
              selection_notes: string
              data_root: string
              best_model_run_used_for_selection: string
              best_model_checkpoint_used_for_selection: string
              official_eval_protocol: struct<pair_sampling: string, pair_stride: int64, max_batches: int64, note: string>
                child 0, pair_sampling: string
                child 1, pair_stride: int64
                child 2, max_batches: int64
                child 3, note: string
              visual_debug_protocol: struct<pair_sampling: string, pair_stride: int64, max_pairs_per_scene: int64, note: string>
                child 0, pair_sampling: string
                child 1, pair_stride: int64
                child 2, max_pairs_per_scene: int64
                child 3, note: string
              selected: list<item: struct<scene: string, path: string, chunk_id: int64, score: double, brightness_mean: doub (... 239 chars omitted)
                child 0, item: struct<scene: string, path: string, chunk_id: int64, score: double, brightness_mean: double, contras (... 227 chars omitted)
                    child 0, scene: string
                    child 1, path: string
                    child 2, chunk_id: int64
                    child 3, score: double
                    child 4, brightness_mean: double
                    child 5, contrast_mean: double
                    child 6, event_active_frac_mean: double
                    child 7, event_abs_mean: double
                    child 8, num_events_median_meta: double
                    child 9, depth_valid_frac_mean: double
                    child 10, depth_median_mean: double
                    child 11, depth_p95_mean: double
                    child 12, trans_0_7_m: double
                    child 13, rot_0_7_deg: double
              name: string
              backup_old_eval_root: string
              split_policy: string
              related_split_manifest: string
              created_on: timestamp[s]
              source_root: string
              excluded_from_test: list<item: struct<scene: string, sequence: string, reason: string>>
                child 0, item: struct<scene: string, sequence: string, reason: string>
                    child 0, scene: string
                    child 1, sequence: string
                    child 2, reason: string
              to
              {'name': Value('string'), 'created_on': Value('timestamp[s]'), 'split_policy': Value('string'), 'selection_notes': Value('string'), 'data_root': Value('string'), 'source_root': Value('string'), 'backup_old_eval_root': Value('string'), 'related_split_manifest': Value('string'), 'official_eval_protocol': {'pair_sampling': Value('string'), 'pair_stride': Value('int64'), 'max_batches': Value('int64'), 'note': Value('string')}, 'visual_debug_protocol': {'pair_sampling': Value('string'), 'pair_stride': Value('int64'), 'max_pairs_per_scene': Value('int64'), 'note': Value('string')}, 'selected': List({'scene': Value('string'), 'path': Value('string'), 'sequence': Value('string'), 'frames': Value('int64'), 'split': Value('string')}), 'excluded_from_test': List({'scene': Value('string'), 'sequence': Value('string'), 'reason': Value('string')})}
              because column names don't match

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Night3R training data

Local copy of /data/fcr/data/train_data, uploaded so the Night3R training and MVSEC/DSEC evaluation roots are not lost. Code: github.com/Passwerob/Night3r. Weights: Passwerob/Night3R.

Directories

Path	Role
`MVSEC/`	MVSEC training scenes.
`DSEC/`	DSEC scenes. Train/test membership is in `night3r_records/dsec_holdout_v1/manifest.json`.
`DL3DV/`	DL3DV training scenes.
`DL3DV_chunk_016_raw/`	Additional DL3DV chunk used by later runs.
`DL3DV_DA3_pseudo_v1/`	DA3 pseudo-depth used by the final training mix. `images/` and `events/` are links into `DL3DV/`.
`DL3DV_DA3_pseudo_chunk016/`	Pseudo-depth for the chunk-016 scenes. Links point into `DL3DV_chunk_016_raw/`.
`MVSEC_outdoor_night2_test/`	Paper MVSEC eval root. Scene entries are links into `MVSEC/`. The final table uses the first 128 fixed adjacent pairs.
`DSEC_test/`	Paper DSEC eval root. Links to `DSEC/screen-000003-dsec`. The final table uses the first 128 fixed adjacent pairs.
`night3r_records/`	Split manifest, baseline summaries, and the resolved final-training config.

NIGHT3R_LINK_MAP.tsv lists every symlink. The first column is the link path inside this dataset. The second column is the target relative to that link's parent, which is what os.symlink expects. Directory links are recorded there instead of duplicating the image and event files.

python restore_night3r_links.py

The held-out DL3DV DA3 reference used for the published DL3DV number, outputs/night3r_dl3dv_da3_pseudo_ft1k_eval_20260722/dl3dv_da3_reference, was already gone locally and is not in this dataset.

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