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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}
}
``` |