Datasets:

Passwerob
/

CoherentGS

ArXiv:

License:

Dataset Viewer

The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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CoherentGS-DL3DV-Blur Dataset

Motivation 💡

To rigorously assess the generalization capability of CoherentGS in complex, unconstrained outdoor environments, we establish a new benchmark named DL3DV-Blur. This benchmark is derived from five diverse scenes within the DL3DV-10K dataset.

Citation Reference: Ling et al. (2024). DL3DV-10K: A Large-scale Dataset for Deep Learning-based 3D Vision.
https://arxiv.org/abs/2312.16256

Dataset Source 🔗

This dataset is constructed from select scenes of the official DL3DV-10K repository.

DL3DV-10K GitHub: https://github.com/DL3DV-10K/Dataset

Data Format 📂

The dataset structure adheres to standard 3D vision dataset formats, where each scene (e.g., 0001) contains sub-folders for different view configurations (e.g., 3views, 6views, 9views).

Structure Overview

The hierarchical structure of the data is as follows:

dl3dv/
├── 0641-0720/
│   ├── 0001/                  # Scene ID 0001
│   │   ├── .work/
│   │   ├── 3views/            # 3-View Sub-set
│   │   │   ├── images/            # Raw input image files 
│   │   │   ├── ref_image/         # Reference Image
│   │   │   ├── sparse/            # Sparse reconstruction results (e.g., COLMAP output)
│   │   │   ├── cameras.json       # Camera parameter file
│   │   │   ├── ext_metadata.json  # Additional metadata
│   │   │   ├── hold=7             # Test set configuration
│   │   │   ├── intrinsics.json    # Camera intrinsics
│   │   │   ├── poses_bounds.npy   # Camera poses and scene bounds
│   │   │   ├── train_test_split_3.json # Train/Test split definition
│   │   │   └── transforms.json    # Coordinate transformation info
│   │   ├── 6views/            # 6-View Sub-set
│   │   └── 9views/            # 9-View Sub-set
│   ├── 0002/
│   ├── 0003/
│   ├── 0004/
│   └── 0005/
└── ...

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