The overall directory structure should be:
│Point-BERT/
├──cfgs/
├──datasets/
├──data/
│ ├──ModelNet/
│ ├──ModelNetFewshot/
│ ├──ScanObjectNN/
│ ├──ShapeNet55-34/
├──.......
ModelNet Dataset: You can download the processed ModelNet data from [Google Drive][Tsinghua Cloud][BaiDuYun](code:4u1e) and save it in data/ModelNet/modelnet40_normal_resampled/. (You can download the offical ModelNet from here, and process it by yourself.) Finally, the directory structure should be:
│ModelNet/
├──modelnet40_normal_resampled/
│ ├── modelnet40_shape_names.txt
│ ├── modelnet40_train.txt
│ ├── modelnet40_test.txt
│ ├── modelnet40_train_8192pts_fps.dat
│ ├── modelnet40_test_8192pts_fps.dat
ModelNet Few-shot Dataset: We follow the previous work to split the original ModelNet40 into pairs of support set and query set. The split used in our experiments is public in [Google Drive]/[Tsinghua Cloud]/[BaiDuYun](code:bjbq). Download the split file and put it into data/ModelNetFewshot, then the structure should be:
│ModelNetFewshot/
├──5way10shot/
│ ├── 0.pkl
│ ├── ...
│ ├── 9.pkl
├──5way20shot/
│ ├── ...
├──10way10shot/
│ ├── ...
├──10way20shot/
│ ├── ...
ShapeNet55/34 Dataset: You can download the processed ShapeNet55/34 dataset at [BaiduCloud] (code:le04) or [Google Drive]. Unzip the file under ShapeNet55-34/. The directory structure should be
│ShapeNet55-34/
├──shapenet_pc/
│ ├── 02691156-1a04e3eab45ca15dd86060f189eb133.npy
│ ├── 02691156-1a6ad7a24bb89733f412783097373bdc.npy
│ ├── .......
├──ShapeNet-35/
│ ├── train.txt
│ └── test.txt
ScanObjectNN Dataset: Download the offical data from here and unzip it into data/ScanObjectNN. The directory structure should be:
│ScanObjectNN/
├──main_split/
│ ├── training_objectdataset_augmentedrot_scale75.h5
│ ├── test_objectdataset_augmentedrot_scale75.h5
│ ├── training_objectdataset.h5
│ ├── test_objectdataset.h5
├──main_split_nobg/
│ ├── training_objectdataset.h5
│ ├── test_objectdataset.h5
ScanNet Dataset: Prepare the pretraining dataset following the instructions from DepthContrast, and place it into data/ScanNet/scannet. To prepare the data faster, you can change the count from 10 to 100 at here and here. The directory structure should be:
│ScanNet/
├──scannet/
│ ├──scene0000_00/
│ ├── 0.npy
│ ├── 100.npy
│ ├── ...
│ ├──scene0000_01/
│ ├── 0.npy
│ ├── 100.npy
│ ├── ...
│ ├──...