Change3D (SLPCCD)
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Notes on the Dataset
- The corresponding paper has been retracted due to errors in the evaluation. A later paper from 2023 again describes the dataset, enriched by synthetic data. However, this second version of the dataset can not be found anywhere.
- The paper speaks of "over 78 [...] point cloud pairs", but the dataset actually contains exactly 78 and for 2020 one additional scan with prefix "15_" that is missing in 2016.
- For computing the label distribution, we extracted cylindrical environments around the POIs and removed the groundplane, as described in the paper. Wang et al. (2023) already provide the data with annotated change labels, which they refer to as the new dataset SLPCCD.
- The acquisition is unclear. The paper states that "The 3D data from CycloMedia are generated from depth maps instead of original LiDAR scans", but the website and the second paper speak of "vehicle mounted LiDAR sensors".
Detailed Information
| General Information | |
| Name | Change3D (SLPCCD) |
| Release Year | 2021 |
| Terms of Use | Unclear |
| Access Requirements | None |
| Dataset Size | 7.6 GB | Partial download is possible, i.e. the data is split into several files (e.g., epochs or data types) |
| Documentation | Multi-paragraph description of the dataset, e.g., in the form of a paper section or a comprehensive readme file |
| Code | Yes |
| Applications | Built environment change detection and classification |
| Detailed Applications | Per-object point cloud change detection |
| Acquisition | |
| Number of Scenes | 78 |
| Number of Epochs per Scene (minimum) | 2 |
| Number of Epochs per Scene (median) | 2 |
| Number of Epochs per Scene (maximum) | 2 |
| General Scene Type | Urban |
| Specific Scene Type | Street segment |
| Location | Schiedam (Netherlands) |
| Acquisition Type | Mobile laser scanning | Educated guess by the authors |
| Acquisition Device | Unclear |
| Acquisition Platform | Car |
| Scan Interval | Years |
| Acquisition Months | |
| Representation | |
| Data Representation | Unstructured, globally aligned (e.g., point cloud or ray cloud) |
| Specific Data Representation | Point cloud |
| File Format/Encoding | LAZ |
| Raw Data | - |
| Additional Data | - |
| Coordinate System | 28992 |
| Quality and Usability | |
| Registration | Finely registered |
| Number of Partial Epochs | 21.80% |
| Unusable Data Reason | - |
| Splits | Yes |
| Per-Point Attributes | |
| Intensity/Reflectivity | No |
| Color | Yes |
| Semantic Labels | - |
| Instance Labels | No |
| Change Labels | 5 | The labels can be computed from other data/mapped to the point cloud, i.e., it can not trivially be done during point cloud construction (at least not for all epochs) |
| Statistics | |
| Number of Points per Epoch (minimum) | 566K |
| Number of Points per Epoch (median) | 710K |
| Number of Points per Epoch (maximum) | 1M |
| Avg. Point Spacing (minimum) | 4.8mm |
| Avg. Point Spacing (median) | 9.8mm |
| Avg. Point Spacing (maximum) | 1.7cm |
| Avg. Change Points per Epoch | 1.43% |
Paper Reference 1
@article{ku2021datasetshrec,
author = {Tao Ku and
Sam Galanakis and
Bas Boom and
Remco C. Veltkamp and
Darshan Bangera and
Shankar Gangisetty and
Nikolaos Stagakis and
Gerasimos Arvanitis and
Konstantinos Moustakas},
title = {SHREC 2021: 3D point cloud change detection for street scenes},
journal = {Comput. Graph.},
volume = {99},
pages = {192--200},
year = {2021},
doi = {10.1016/J.CAG.2021.07.004},
}
Paper Reference 2
@article{wang2023datasetslpccd,
author = {Zhixue Wang and
Yu Zhang and
Lin Luo and
Kai Yang and
Liming Xie},
title = {An End-to-End Point-Based Method and a New Dataset for Street-Level
Point Cloud Change Detection},
journal = {IEEE Trans. Geosci. Remote. Sens.},
volume = {61},
pages = {1--15},
year = {2023},
doi = {10.1109/TGRS.2023.3295386},
}