Datasets:

LiYacheng
/

r4d-bench-qa-v2

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
query_id: string
text_en: string
text_zh: string
target_track_ids: list<item: string>
  child 0, item: string
target_count: int64
requires_motion_understanding: bool
query_type: string
temporal_relation: string
expected_temporal_anchor: string
scene: string
note: string
distractor: string
keyboard_q3: list<item: string>
  child 0, item: string
keyboard_q5: list<item: null>
  child 0, item: null
coffee_martini_q4: list<item: string>
  child 0, item: string
flame_steak_q3: list<item: string>
  child 0, item: string
flame_steak_q2: list<item: string>
  child 0, item: string
espresso_q1: list<item: string>
  child 0, item: string
torchchocolate_q5: list<item: null>
  child 0, item: null
keyboard_q2: list<item: string>
  child 0, item: string
cook-spinach_q4: list<item: string>
  child 0, item: string
sear_steak_q2: list<item: string>
  child 0, item: string
flame_steak_q7: list<item: string>
  child 0, item: string
espresso_q8: list<item: string>
  child 0, item: string
torchchocolate_q3: list<item: string>
  child 0, item: string
sear_steak_q4: list<item: string>
  child 0, item: string
cut_lemon_q5: list<item: string>
  child 0, item: string
americano_q8: list<item: null>
  child 0, item: null
cut_lemon_q3: list<item: string>
  child 0, item: string
espresso_q6: list<item: null>
  child 0, item: null
cut_roasted_beef_q5: list<item: null>
  child 0, item: null
sear_steak_q6: list<item: null>
  child 0, item: null
torchchocolate_q7: list<item: string>
  child 0, item: string
amer
...
fee_martini_q1: list<item: string>
  child 0, item: string
coffee_martini_q6: list<item: null>
  child 0, item: null
espresso_q2: list<item: string>
  child 0, item: string
cut_lemon_q1: list<item: string>
  child 0, item: string
cook-spinach_q6: list<item: null>
  child 0, item: null
coffee_martini_q5: list<item: null>
  child 0, item: null
torchchocolate_q6: list<item: string>
  child 0, item: string
torchchocolate_q4: list<item: null>
  child 0, item: null
espresso_q3: list<item: string>
  child 0, item: string
cut_roasted_beef_q6: list<item: null>
  child 0, item: null
espresso_q4: list<item: string>
  child 0, item: string
americano_q7: list<item: null>
  child 0, item: null
americano_q3: list<item: string>
  child 0, item: string
flame_steak_q5: list<item: null>
  child 0, item: null
cook-spinach_q2: list<item: string>
  child 0, item: string
flame_salmon_q4: list<item: string>
  child 0, item: string
coffee_martini_q3: list<item: string>
  child 0, item: string
cut_lemon_q8: list<item: null>
  child 0, item: null
coffee_martini_q2: list<item: string>
  child 0, item: string
sear_steak_q5: list<item: null>
  child 0, item: null
keyboard_q1: list<item: string>
  child 0, item: string
cut_lemon_q7: list<item: null>
  child 0, item: null
cut_roasted_beef_q3: list<item: string>
  child 0, item: string
flame_salmon_q5: list<item: null>
  child 0, item: null
cook-spinach_q3: list<item: string>
  child 0, item: string
split_cookie_q7: list<item: string>
  child 0, item: string
to
{'cut_lemon_q1': List(Value('string')), 'cut_lemon_q2': List(Value('string')), 'cut_lemon_q3': List(Value('string')), 'cut_lemon_q4': List(Value('string')), 'cut_lemon_q5': List(Value('string')), 'cut_lemon_q6': List(Value('string')), 'cut_lemon_q7': List(Value('null')), 'cut_lemon_q8': List(Value('null')), 'espresso_q1': List(Value('string')), 'espresso_q2': List(Value('string')), 'espresso_q3': List(Value('string')), 'espresso_q4': List(Value('string')), 'espresso_q5': List(Value('null')), 'espresso_q6': List(Value('null')), 'espresso_q7': List(Value('string')), 'espresso_q8': List(Value('string')), 'keyboard_q1': List(Value('string')), 'keyboard_q2': List(Value('string')), 'keyboard_q3': List(Value('string')), 'keyboard_q4': List(Value('null')), 'keyboard_q5': List(Value('null')), 'keyboard_q6': List(Value('string')), 'torchchocolate_q1': List(Value('string')), 'torchchocolate_q2': List(Value('string')), 'torchchocolate_q3': List(Value('string')), 'torchchocolate_q4': List(Value('null')), 'torchchocolate_q5': List(Value('null')), 'torchchocolate_q6': List(Value('string')), 'torchchocolate_q7': List(Value('string')), 'cook-spinach_q1': List(Value('string')), 'cook-spinach_q2': List(Value('string')), 'cook-spinach_q3': List(Value('string')), 'cook-spinach_q4': List(Value('string')), 'cook-spinach_q5': List(Value('null')), 'cook-spinach_q6': List(Value('null')), 'cook-spinach_q7': List(Value('string')), 'cut_roasted_beef_q1': List(Value('string')), 'cut_roasted_beef_q2': List
...
sear_steak_q3': List(Value('string')), 'sear_steak_q4': List(Value('string')), 'sear_steak_q5': List(Value('null')), 'sear_steak_q6': List(Value('null')), 'sear_steak_q7': List(Value('string')), 'split_cookie_q1': List(Value('string')), 'split_cookie_q2': List(Value('string')), 'split_cookie_q3': List(Value('string')), 'split_cookie_q4': List(Value('string')), 'split_cookie_q5': List(Value('null')), 'split_cookie_q6': List(Value('string')), 'split_cookie_q7': List(Value('string')), 'split_cookie_q8': List(Value('null')), 'americano_q1': List(Value('string')), 'americano_q2': List(Value('string')), 'americano_q3': List(Value('string')), 'americano_q4': List(Value('string')), 'americano_q5': List(Value('string')), 'americano_q6': List(Value('string')), 'americano_q7': List(Value('null')), 'americano_q8': List(Value('null')), 'americano_q9': List(Value('string')), 'americano_q10': List(Value('string')), 'coffee_martini_q1': List(Value('string')), 'coffee_martini_q2': List(Value('string')), 'coffee_martini_q3': List(Value('string')), 'coffee_martini_q4': List(Value('string')), 'coffee_martini_q5': List(Value('null')), 'coffee_martini_q6': List(Value('null')), 'coffee_martini_q7': List(Value('string')), 'flame_steak_q1': List(Value('string')), 'flame_steak_q2': List(Value('string')), 'flame_steak_q3': List(Value('string')), 'flame_steak_q4': List(Value('string')), 'flame_steak_q5': List(Value('null')), 'flame_steak_q6': List(Value('null')), 'flame_steak_q7': List(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
              query_id: string
              text_en: string
              text_zh: string
              target_track_ids: list<item: string>
                child 0, item: string
              target_count: int64
              requires_motion_understanding: bool
              query_type: string
              temporal_relation: string
              expected_temporal_anchor: string
              scene: string
              note: string
              distractor: string
              keyboard_q3: list<item: string>
                child 0, item: string
              keyboard_q5: list<item: null>
                child 0, item: null
              coffee_martini_q4: list<item: string>
                child 0, item: string
              flame_steak_q3: list<item: string>
                child 0, item: string
              flame_steak_q2: list<item: string>
                child 0, item: string
              espresso_q1: list<item: string>
                child 0, item: string
              torchchocolate_q5: list<item: null>
                child 0, item: null
              keyboard_q2: list<item: string>
                child 0, item: string
              cook-spinach_q4: list<item: string>
                child 0, item: string
              sear_steak_q2: list<item: string>
                child 0, item: string
              flame_steak_q7: list<item: string>
                child 0, item: string
              espresso_q8: list<item: string>
                child 0, item: string
              torchchocolate_q3: list<item: string>
                child 0, item: string
              sear_steak_q4: list<item: string>
                child 0, item: string
              cut_lemon_q5: list<item: string>
                child 0, item: string
              americano_q8: list<item: null>
                child 0, item: null
              cut_lemon_q3: list<item: string>
                child 0, item: string
              espresso_q6: list<item: null>
                child 0, item: null
              cut_roasted_beef_q5: list<item: null>
                child 0, item: null
              sear_steak_q6: list<item: null>
                child 0, item: null
              torchchocolate_q7: list<item: string>
                child 0, item: string
              amer
              ...
              fee_martini_q1: list<item: string>
                child 0, item: string
              coffee_martini_q6: list<item: null>
                child 0, item: null
              espresso_q2: list<item: string>
                child 0, item: string
              cut_lemon_q1: list<item: string>
                child 0, item: string
              cook-spinach_q6: list<item: null>
                child 0, item: null
              coffee_martini_q5: list<item: null>
                child 0, item: null
              torchchocolate_q6: list<item: string>
                child 0, item: string
              torchchocolate_q4: list<item: null>
                child 0, item: null
              espresso_q3: list<item: string>
                child 0, item: string
              cut_roasted_beef_q6: list<item: null>
                child 0, item: null
              espresso_q4: list<item: string>
                child 0, item: string
              americano_q7: list<item: null>
                child 0, item: null
              americano_q3: list<item: string>
                child 0, item: string
              flame_steak_q5: list<item: null>
                child 0, item: null
              cook-spinach_q2: list<item: string>
                child 0, item: string
              flame_salmon_q4: list<item: string>
                child 0, item: string
              coffee_martini_q3: list<item: string>
                child 0, item: string
              cut_lemon_q8: list<item: null>
                child 0, item: null
              coffee_martini_q2: list<item: string>
                child 0, item: string
              sear_steak_q5: list<item: null>
                child 0, item: null
              keyboard_q1: list<item: string>
                child 0, item: string
              cut_lemon_q7: list<item: null>
                child 0, item: null
              cut_roasted_beef_q3: list<item: string>
                child 0, item: string
              flame_salmon_q5: list<item: null>
                child 0, item: null
              cook-spinach_q3: list<item: string>
                child 0, item: string
              split_cookie_q7: list<item: string>
                child 0, item: string
              to
              {'cut_lemon_q1': List(Value('string')), 'cut_lemon_q2': List(Value('string')), 'cut_lemon_q3': List(Value('string')), 'cut_lemon_q4': List(Value('string')), 'cut_lemon_q5': List(Value('string')), 'cut_lemon_q6': List(Value('string')), 'cut_lemon_q7': List(Value('null')), 'cut_lemon_q8': List(Value('null')), 'espresso_q1': List(Value('string')), 'espresso_q2': List(Value('string')), 'espresso_q3': List(Value('string')), 'espresso_q4': List(Value('string')), 'espresso_q5': List(Value('null')), 'espresso_q6': List(Value('null')), 'espresso_q7': List(Value('string')), 'espresso_q8': List(Value('string')), 'keyboard_q1': List(Value('string')), 'keyboard_q2': List(Value('string')), 'keyboard_q3': List(Value('string')), 'keyboard_q4': List(Value('null')), 'keyboard_q5': List(Value('null')), 'keyboard_q6': List(Value('string')), 'torchchocolate_q1': List(Value('string')), 'torchchocolate_q2': List(Value('string')), 'torchchocolate_q3': List(Value('string')), 'torchchocolate_q4': List(Value('null')), 'torchchocolate_q5': List(Value('null')), 'torchchocolate_q6': List(Value('string')), 'torchchocolate_q7': List(Value('string')), 'cook-spinach_q1': List(Value('string')), 'cook-spinach_q2': List(Value('string')), 'cook-spinach_q3': List(Value('string')), 'cook-spinach_q4': List(Value('string')), 'cook-spinach_q5': List(Value('null')), 'cook-spinach_q6': List(Value('null')), 'cook-spinach_q7': List(Value('string')), 'cut_roasted_beef_q1': List(Value('string')), 'cut_roasted_beef_q2': List
              ...
              sear_steak_q3': List(Value('string')), 'sear_steak_q4': List(Value('string')), 'sear_steak_q5': List(Value('null')), 'sear_steak_q6': List(Value('null')), 'sear_steak_q7': List(Value('string')), 'split_cookie_q1': List(Value('string')), 'split_cookie_q2': List(Value('string')), 'split_cookie_q3': List(Value('string')), 'split_cookie_q4': List(Value('string')), 'split_cookie_q5': List(Value('null')), 'split_cookie_q6': List(Value('string')), 'split_cookie_q7': List(Value('string')), 'split_cookie_q8': List(Value('null')), 'americano_q1': List(Value('string')), 'americano_q2': List(Value('string')), 'americano_q3': List(Value('string')), 'americano_q4': List(Value('string')), 'americano_q5': List(Value('string')), 'americano_q6': List(Value('string')), 'americano_q7': List(Value('null')), 'americano_q8': List(Value('null')), 'americano_q9': List(Value('string')), 'americano_q10': List(Value('string')), 'coffee_martini_q1': List(Value('string')), 'coffee_martini_q2': List(Value('string')), 'coffee_martini_q3': List(Value('string')), 'coffee_martini_q4': List(Value('string')), 'coffee_martini_q5': List(Value('null')), 'coffee_martini_q6': List(Value('null')), 'coffee_martini_q7': List(Value('string')), 'flame_steak_q1': List(Value('string')), 'flame_steak_q2': List(Value('string')), 'flame_steak_q3': List(Value('string')), 'flame_steak_q4': List(Value('string')), 'flame_steak_q5': List(Value('null')), 'flame_steak_q6': List(Value('null')), 'flame_steak_q7': List(Value('string'))}
              because column names don't match

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R4D-Bench

Hub mirrors (same content):

https://huggingface.co/datasets/LiYacheng/r4d-bench-qa-v2 （recommended）
https://huggingface.co/datasets/LiYacheng/r4d-bench-qa

What this dataset is

R4D-Bench is a benchmark for spatio-temporal referring segmentation in dynamic (4D) scenes: given a natural-language query, the model must return the correct instance mask(s) over time.

This release ships:

Shipped	Count / note
Scenes	12 under `data/scenes/`
Referring queries	89 (bilingual `text_en` / `text_zh`)
Dense mask GT	Per-query, per-frame polygons/RLE + PNG unions under `query_masks/`
Temporal gating	Outside `expected_temporal_anchor` windows → black / empty masks (same frame count)
Eval scripts	Mask IoU (`evaluate_mask_temporal.py`) and track-ID F1 (`evaluate_r4dgs.py`)
Optional subset	36 queries (3 / scene) for quick debugging

Not required for the shipped metrics: COLMAP, cameras, Segment-then-Splat outputs, or a full multi-view training tree. Original Neu3D / HyperNeRF imagery (when you need more RGB beyond what is in each scene folder) stays under upstream licenses.

Hugging Face Dataset Viewer: this repo is a folder-layout benchmark (JSON + PNG masks), not a tabular Parquet split. The Hub “Dataset Viewer / test split” panel will stay unavailable — that is expected. Use Files and versions and the commands below.

Quick start (3 steps)

# 1) Download this dataset repo (Hub UI, git lfs, or huggingface_hub snapshot_download)

# 2) Sanity-check mask metrics (predictions = GT → perfect scores)
python evaluation/evaluate_mask_temporal.py --self-check \
  --ground-truth scripts/new_predictions_ground_truth_all_queries.json \
  --queries-meta evaluation/R4D-Bench_queries.json

# 3) Track-ID metrics (GT track sets as a demo)
python evaluation/evaluate_r4dgs.py \
  --queries evaluation/R4D-Bench_queries.json \
  --predictions evaluation/R4D-Bench_predictions.json \
  --output evaluation/track_id_eval_report.json

Dependencies: Python 3.10+; numpy, Pillow for mask eval. Evaluation scripts live next to the data — run them from the repo root.

Which files to open first

Goal	Open this
Read all 89 queries (text + target tracks)	`evaluation/R4D-Bench_queries.json`
Track-ID ground truth map	`evaluation/R4D-Bench_predictions.json` (`query_id` → `target_track_ids`)
Dense mask ground truth (canonical, 89)	`scripts/new_predictions_ground_truth_all_queries.json`
Same GT + PNG path fields	`scripts/new_predictions_ground_truth_all_queries_with_paths.json`
Per-scene query source of truth	`data/scenes/<scene>/*_queries.json`
Per-frame answer PNGs	`data/scenes/<scene>/query_masks/frame_XXXXXX/<query_id>/…`
Track id → category cheat sheet	`data/track_metadata.csv` (docs only; not loaded by eval)
Path checklist	`DATASET_LAYOUT.md`

Optional 36-query subset: scripts/new_predictions_ground_truth_final.json (+ *_with_paths.json). Same format, fewer queries — not a separate release tier.

Scenes

Folders under data/scenes/ (underscores):

americano, coffee_martini, cook_spinach, cut_lemon, cut_roasted_beef, espresso, flame_salmon, flame_steak, keyboard, sear_steak, split_cookie, torchchocolate

Each scene typically contains:

*_queries.json — queries for that scene
scene track JSON (e.g. americano.json; cook_spinach uses cook-spinach.json)
COCO annotation JSON (_annotations*.coco.json)
images/ — RGB frames used with the annotations
query_masks/ — query-level union masks for the 89-query set
query_masks_36/ — masks for the optional 36-query subset (when present)
README.dataset.txt / README.roboflow.txt — upstream provenance

Naming: folder names use _; some JSON / query_id prefixes use - (e.g. cook-spinach, split-cookie).

Task definition & conventions

Input: referring expression (text_en / text_zh) + scene frames.
Output (mask track): per-frame_id foreground mask(s) for the referred target(s).
Output (track-ID track): set of target_track_ids (no pixels).
Time index: integer frame_id in GT / existence_frames, matching query_masks/frame_XXXXXX/.
Left / right: means image / viewer coordinates (left or right side of the picture), not the person’s anatomical left/right hand.
Query types: A / B / C in metadata (see fields below); many items need motion or before / while / after reasoning; some are multi-target or distractor-style.

Temporal mask GT (strict gating)

Dense mask GT keeps the same frame list as the referent track’s COCO-annotated frames (so queries are comparable in length). When a query sets expected_temporal_anchor, that name is looked up in the target track’s temporal_profile in the scene track JSON:

Inside the anchor interval(s): normal object mask (white / polygons).
Outside the interval but still in the track’s annotated span: empty / black full-frame mask (temporally_inactive: true).
existence_frames: only frames with a positive (non-empty) mask.
frames[]: all scored frames, including black ones.
Type C distractors: still empty on every synthesized scene frame.

Eval already uses: both empty → IoU 1.0; predicting an object on a black GT frame → IoU 0.0. So models must get both identity and timing right.

Example: sear_steak_q2 still has 16 frames, but only 2 positive (existence_frames ≈ 280–299); the other 14 are black.

Temporal states that must be distinguished in mask eval use different profile keys (e.g. darkening vs fully_darkened, solid vs melting, breaking vs broken_pieces), not one shared label for before/while/after. Cut points for those keys were RGB-calibrated on sampled frames (not sparse-sampling artifacts).

Static / never-move queries keep full-span positive masks (no empty gating). Type C / zero-target queries stay empty on all frames.

Query schema (`evaluation/R4D-Bench_queries.json`)

Each entry includes among others:

query_id, scene, text_en, text_zh
target_track_ids, target_count
query_type, requires_motion_understanding
Optional: temporal_relation, expected_temporal_anchor, distractor, note

Dense GT entries in scripts/new_predictions_ground_truth_all_queries.json add ground_truth with target_tracks, existence_frames, and frames[].masks[] (segmentation, optional mask_image). Geometry in segmentation / PNGs is authoritative.

How to evaluate your model

1) Mask-level (mIoU, mAcc, temporal Acc, vIoU, …)

python evaluation/evaluate_mask_temporal.py \
  --predictions path/to/your_predictions.json \
  --ground-truth scripts/new_predictions_ground_truth_all_queries.json \
  --output evaluation/mask_eval_report.json \
  --queries-meta evaluation/R4D-Bench_queries.json

Prediction JSON (either shape):

{ "query_id": { "frames": { "1": "path/to/mask.png", ... } } }
or a list of { "frame_id", "mask_path" } per query

Paths resolve relative to --repo-root unless absolute. Useful flags: --only-query-prefix, --only-queries, --debug-query, --iou-threshold.

2) Track-ID (precision / recall / F1 on ID sets)

python evaluation/evaluate_r4dgs.py \
  --queries evaluation/R4D-Bench_queries.json \
  --predictions path/to/your_track_predictions.json \
  --output evaluation/track_id_eval_report.json

Your prediction file should use the same query_id keys as evaluation/R4D-Bench_predictions.json (each value = predicted track-id list/set).

Repository layout

R4D-Bench/
├── DATASET_LAYOUT.md
├── README.md
├── evaluation/
│   ├── evaluate_mask_temporal.py
│   ├── evaluate_r4dgs.py
│   ├── R4D-Bench_queries.json      # 89 queries (eval metadata)
│   └── R4D-Bench_predictions.json  # query_id → target_track_ids (GT)
├── scripts/
│   ├── coco_scene_paths.py
│   ├── generate_instance_masks.py
│   ├── predictions_ground_truth.py
│   ├── enrich_ground_truth_with_mask_images.py
│   ├── new_predictions_ground_truth_all_queries.json
│   ├── new_predictions_ground_truth_all_queries_with_paths.json
│   ├── new_predictions_ground_truth_final.json              # optional 36
│   └── new_predictions_ground_truth_final_with_paths.json
└── data/
    ├── scenes/<scene>/    # images, COCO, tracks, queries, query_masks/
    ├── all_instance_masks/  # optional regeneratable snapshot
    └── track_metadata.csv   # optional human-readable reference

upload_large.py is a maintainer helper for Hub uploads; end users can ignore it.

Regenerating masks / GT (maintainers)

# Per-instance PNGs from COCO (not the same as query union masks)
python scripts/generate_instance_masks.py --overwrite

# Rebuild 89-query dense GT from per-scene *_queries.json
python scripts/predictions_ground_truth.py --all-queries \
  --output scripts/new_predictions_ground_truth_all_queries.json

# Add mask_image / combined_mask_image path fields into JSON
python scripts/enrich_ground_truth_with_mask_images.py

Relation to Segment-then-Splat & upstream data

Segment-then-Splat is a separate pipeline. Typical StS trees (sparse/, merged multiview masks, PLY, …) are not part of this minimal annotation release.
Pair with HyperNeRF / Neu3D (or compatible) downloads under their original licenses when you need the full original capture.
Roboflow URLs / licenses: see each scene’s README.dataset.txt / README.roboflow.txt.

Citation

If you use R4D-Bench, cite this dataset repository and the original scene datasets (HyperNeRF, Neu3D, Roboflow sources as applicable). Add BibTeX here after publication if desired.

License

Annotations and code in this repository: treat as license: other until a top-level LICENSE is added; contact maintainers if you need redistributable terms in writing.
Scene imagery / upstream assets remain under Neu3D / HyperNeRF / Roboflow (etc.) original terms. Do not redistribute raw imagery unless those licenses allow it.

Contact

Questions on benchmark definition, evaluation protocol, or file formats: open a discussion/issue on this Hub dataset page or contact the maintainers.

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