open3d.ml.torch.datasets.TUMFacade#

class open3d.ml.torch.datasets.TUMFacade(dataset_path, info_path=None, name='TUM_Facade', cache_dir='./logs/cache', use_cache=False, use_global=False, **kwargs)#
__init__(dataset_path, info_path=None, name='TUM_Facade', cache_dir='./logs/cache', use_cache=False, use_global=False, **kwargs)#

Dataset classes for the TUM-Facade dataset. Semantic segmentation annotations over TUM-MLS-2016 point cloud data.

Website: https://mediatum.ub.tum.de/node?id=1636761 Code: https://github.com/OloOcki/tum-facade Download:

Data License: CC BY-NC-SA 4.0 Citation:

  • Paper: Wysocki, O. and Hoegner, L. and Stilla, U., TUM-FAÇADE: Reviewing and enriching point cloud benchmarks for façade segmentation, ISPRS 2022

  • Dataset: Wysocki, Olaf and Tan, Yue and Zhang, Jiarui and Stilla, Uwe, TUM-FACADE dataset, TU Munich, 2023

README file from processed dataset website:

The dataset split is provided in the following folder structure

–>tum-facade
–>pointclouds
–>annotatedGlobalCRS

–>test_files –>training_files –>validation_files

–>annotatedLocalCRS

–>test_files –>training_files –>validation_file

The indivisual point clouds are compressed as .7z files and are stored in the .pcd format.

To make use of the dataset split in open3D-ML, all the point cloud files have to be unpacked with 7Zip. The folder structure itself must not be modified, else the reading functionalities in open3D-ML are not going to work. As a path to the dataset, the path to the ‘tum-facade’ folder must be set.

The dataset is split in the following way (10.08.2023):

Testing : Building Nr. 23 Training : Buildings Nr. 57, Nr.58, Nr. 60 Validation : Buildings Nr. 22, Nr.59, Nr. 62, Nr. 81

Initialize the function by passing the dataset and other details.

Parameters:

  • dataset_path – The path to the dataset to use.

  • info_path – The path to the file that includes information about the dataset. This is default to dataset path if nothing is provided.

  • name – The name of the dataset (TUM_Facade in this case).

  • cache_dir – The directory where the cache is stored.

  • use_cache – Indicates if the dataset should be cached.

  • use_global – Inidcates if the dataset should be used in a local or the global CRS

Returns:

The corresponding class.

Return type:

class

static get_label_to_names()#

Returns a label to names dictionary object.

Returns:

A dict where keys are label numbers and values are the corresponding names.

get_split(split)#

Returns a dataset split.

Parameters:

  • split – A string identifying the dataset split that is usually one of

  • 'training' –

  • 'test' –

  • 'validation' –

  • 'all'. (or) –

Returns:

A dataset split object providing the requested subset of the data.

get_split_list(split)#

Returns the list of data splits available.

Parameters:

  • split – A string identifying the dataset split that is usually one of

  • 'training' –

  • 'test' –

  • 'validation' –

  • 'all'. (or) –

Returns:

A dataset split object providing the requested subset of the data.

Raises:

  • ValueError – Indicates that the split name passed is incorrect. The

  • split name should be one of 'training', 'test', 'validation', or –

  • 'all'. –

is_tested(attr)#

Checks whether a datum has been tested.

Parameters:

attr – The attributes associated with the datum.

Returns:

This returns True if the test result has been stored for the datum with the specified attribute; else returns False.

save_test_result(results, attr)#

Saves the output of a model.

Parameters:

  • results – The output of a model for the datum associated with the attribute passed.

  • attr – The attributes that correspond to the outputs passed in results.