| --- |
| task_categories: |
| - image-classification |
| - point-cloud-classification |
| - point-cloud-recognition |
| --- |
| |
| ## Datasets |
| 1. We conduct experiments on three new 3D domain generalization ([3DDG](#new-3ddg-benchmarks)) benchmarks proposed by us, as introduced in the next section. |
| - base-to-new class generalization (base2new) |
| - cross-dataset generalization (xset) |
| - few-shot generalization (fewshot) |
|
|
| 2. The structure of these benchmarks should be organized as follows. |
| ```sh |
| /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 |
| ``` |
|
|
| 3. 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_ |
| 1. The datasets used in this benchmark can be downloaded according to the following links. |
| - [ModelNet40](https://huggingface.co/datasets/auniquesun/Point-PRC/tree/main/new-3ddg-benchmarks/base2new/modelnet40) |
| - [S-OBJ_ONLY](https://huggingface.co/datasets/auniquesun/Point-PRC/tree/main/new-3ddg-benchmarks/base2new/scanobjectnn/obj_only) |
| - [S-OBJ_BG](https://huggingface.co/datasets/auniquesun/Point-PRC/tree/main/new-3ddg-benchmarks/base2new/scanobjectnn/obj_bg) |
| - [S-PB_T50_RS](https://huggingface.co/datasets/auniquesun/Point-PRC/tree/main/new-3ddg-benchmarks/base2new/scanobjectnn/hardest) |
| - [ShapeNetCoreV2](https://huggingface.co/datasets/auniquesun/Point-PRC/tree/main/new-3ddg-benchmarks/base2new/shapenetcorev2) |
|
|
| 2. The following table shows the statistics of this benchmark. |
|
|
|  |
| |
| ### _Cross-dataset Generalization_ |
| 1. The datasets used in this benchmark can be downloaded according to the following links. |
| - [OOD Generalization](https://huggingface.co/datasets/auniquesun/Point-PRC/tree/main/new-3ddg-benchmarks/xset/dg) |
| - OmniObject3d (Omin3D) |
| - [Data Corruption](https://huggingface.co/datasets/auniquesun/Point-PRC/tree/main/new-3ddg-benchmarks/xset/corruption) |
| - ModelNet-C (7 types of corruptions) |
| - add global outliers, add local outliers, dropout global structure, dropout local region, rotation, scaling, jittering |
| - [Sim-to-Real](https://huggingface.co/datasets/auniquesun/Point-PRC/tree/main/new-3ddg-benchmarks/xset/sim2real) |
| - [PointDA](https://huggingface.co/datasets/auniquesun/Point-PRC/tree/main/new-3ddg-benchmarks/xset/pointda) |
|
|
| 2. The following table shows the statistics of this benchmark. |
|
|
|  |
| |
| ### _Few-shot Generalization_ |
| 1. 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_. |
|
|
| 2. The following table shows the statistics of this benchmark. |
|
|
|  |
| |
| ## Citation |
| 1. If you find our paper and datasets are helpful for your project or research, please cite our work as follows. |
| ``` |
| @inproceedings{sun24pointprc, |
| title={Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis}, |
| author={Sun, Hongyu and Ke, Qiuhong and Wang, Yongcai and Chen, Wang and Yang, Kang and Li, Deying and Cai, Jianfei}, |
| booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS)}, |
| year={2024}, |
| url={https://openreview.net/forum?id=g7lYP11Erv} |
| } |
| ``` |
