task_categories:
- image-classification
license: mit
Datasets
We conduct experiments on three new 3D domain generalization (3DDG) benchmarks proposed by us, as introduced in the next section.
- base-to-new class generalization (base2new)
- cross-dataset generalization (xset)
- few-shot generalization (fewshot)
The structure of these benchmarks should be organized as follows.
/path/to/Point-PRC
|----data # placed in the same level of `trainers`, `weights`, etc.
|----base2new
|----modelnet40
|----scanobjectnn
|----shapenetcorev2
|----xset
|----corruption
|----dg
|----sim2real
|----pointda
|----fewshot
|----modelnet40
|----scanobjectnn
|----shapenetcorev2
- You can find the usage instructions and download links of these new 3DDG benchmarks in the following section.
New 3DDG Benchmarks
Base-to-new Class Generalization
The datasets used in this benchmark can be downloaded according to the following links.
The following table shows the statistics of this benchmark.
Cross-dataset Generalization
The datasets used in this benchmark can be downloaded according to the following links.
- OOD Generalization
- OmniObject3d (Omin3D)
- Data Corruption
- ModelNet-C (7 types of corruptions)
- add global outliers, add local outliers, dropout global structure, dropout local region, rotation, scaling, jittering
- ModelNet-C (7 types of corruptions)
- Sim-to-Real
- PointDA
- OOD Generalization
The following table shows the statistics of this benchmark.
Few-shot Generalization
Although this benchmark contains same datasets as the Base-to-new Class, it investigates the model generalization under extremely low-data regime (1, 2, 4, 8, and 16 shots), which is quite different from the evaluation setting in Base-to-new Class Generalization.
The following table shows the statistics of this benchmark.
Dataset presented in Point-Cache: Test-time Dynamic and Hierarchical Cache for Robust and Generalizable Point Cloud Analysis.