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.