Datasets:

haofeixu
/

pointdit-eval

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label pointdit-eval@312559c89f3b86925e3286ca100c7b0f435e7399
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2386, 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 2303, 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 2178, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1460, 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 1483, 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 1158, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1095, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1116, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label pointdit-eval@312559c89f3b86925e3286ca100c7b0f435e7399

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PointDiT Evaluation Dataset: Booster (Monocular Split)

This repository contains the preprocessed evaluation split of the Booster Dataset (Monocular Split) used for evaluating monocular geometry and 3D point estimation models.

Dataset Overview

Dataset: Booster Dataset (Monocular Split)
Source: AMS Acta (DOI: 10.6092/unibo/amsacta/7161)
Authors: Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi, Costanzino Alex, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano (University of Bologna, 2023)
License: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Description: High-resolution indoor scenes featuring challenging non-Lambertian surfaces (specular and transparent materials) and fine structures, prepared for evaluating monocular depth and 3D geometry estimation.

Preprocessing Details

The raw data has been adapted for monocular 3D evaluation:

Standardized image dimensions and aspect ratios.
Extracted and formatted ground-truth point maps, depth, and camera intrinsics.
Structured testing splits for direct evaluation in point and depth estimation benchmarks.

License & Terms of Use

This dataset is distributed under the CC BY-NC 4.0 license in accordance with the original source repository.

Non-Commercial: The data cannot be used for commercial purposes.
Attribution: You must give appropriate credit to the original authors, provide a link to the license, and indicate if changes were made.

If you use this benchmark data in your research, please cite the original dataset paper.

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