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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label multicam-4d-stereo@45c4cca072564029310741c649eda097184e6f9f
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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label multicam-4d-stereo@45c4cca072564029310741c649eda097184e6f9f

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MultiCam 4D Stereo Dataset

同步 8 目立体相机 4D 采集数据。8 台 DECXIN/Nori 并排立体模组由单个 ESP32 门控触发器并联驱动,跨相机同步 ~0.4µs(亚微秒)。每台相机自带用屏幕 ChArUco 标定出的双目内参

相机

每台相机有唯一动物名(对应物理标签):Cat Pig Cow Fish Tiger Sheep Dog Mouse

文件(每个 zip = 一段截取)

文件 会话 帧范围 每台每目帧数
ui_1784818172_8s到结尾_8台命名带内参.zip ui_1784818172 8s→结尾 353
ui_1784817901_4s到7s_8台命名带内参.zip ui_1784817901 4s→7s 90
ui_1784817901_10s到结尾_8台命名带内参.zip ui_1784817901 10s→结尾 291

(帧范围按 30fps 换算;每 zip 解压得到一个 dataset_<session>_<range>/ 顶层目录。)

目录结构

dataset_<session>_<range>/
├── capture_info.json          # 元信息(相机/帧范围/同步抖动/含标定清单)
├── README.txt                 # 用法说明
├── Tiger/
│   ├── left/   000000.jpg … NNNNNN.jpg
│   ├── right/  000000.jpg … NNNNNN.jpg
│   └── calibration/
│       ├── foundationstereo_K.txt        # 校正后 K + 基线(FoundationStereo 格式)
│       ├── stereo_calibration.npz         # K/D/R/T + 极线校正映射 left_map/right_map
│       └── stereo_calibration_summary.json
├── Fish/ … Cat/ … Pig/ … Cow/ … Sheep/ … Dog/ … Mouse/ …

对齐约定

  • 同一相机 left/NNNNNN.jpgright/NNNNNN.jpg = 同一原始帧
  • 不同相机的同名帧 = 同一触发瞬间(亚微秒同步)。
  • left/right 为原始拆分(相机倒装已转正,未极线校正);视差约定 x_left - x_right > 0,符合 FoundationStereo。

使用 (FoundationStereo)

送 FoundationStereo 前,对每台的 left/right 用其 calibration/stereo_calibration.npz 里的 left_map/right_mapcv2.remap 极线校正,再配 foundationstereo_K.txt:

import numpy as np, cv2
c = np.load("Tiger/calibration/stereo_calibration.npz")
L = cv2.remap(cv2.imread("Tiger/left/000000.jpg"),  c["left_map_x"],  c["left_map_y"],  cv2.INTER_LINEAR)
R = cv2.remap(cv2.imread("Tiger/right/000000.jpg"), c["right_map_x"], c["right_map_y"], cv2.INTER_LINEAR)
# 校正后的 L/R + foundationstereo_K.txt -> FoundationStereo

标定质量

每台屏幕 ChArUco 双目标定:stereo RMS ≈ 0.6px、基线 ≈ 59.5mm(8 台聚集在 59.35–59.83mm,硬件一致)。

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