Point cloud classification on ModelNet40

Note in this experiment, we do not use any re-sampled version of ModelNet40 (more than 2K points) or any normal information. The data we use is: modelnet40_ply_hdf5_2048[1].

Dataset

ModelNet40 dataset will be downloaded automatically.

Train

For example, train PointNeXt-S

CUDA_VISIBLE_DEVICES=0 python examples/classification/main.py --cfg cfgs/modelnet40ply2048/pointnext-s.yaml

Test

test PointNeXt-S (C=64)

CUDA_VISIBLE_DEVICES=0 python examples/classification/main.py --cfg cfgs/modelnet40ply2048/pointnext-s.yaml model.encoder_args.width=64 mode=test --pretrained_path /path/to/your/pretrained_model

Reference

@InProceedings{wu2015modelnet,
    author = {Wu, Zhirong and Song, Shuran and Khosla, Aditya and Yu, Fisher and Zhang, Linguang and Tang, Xiaoou and Xiao, Jianxiong},
    title = {3D ShapeNets: A Deep Representation for Volumetric Shapes},
    booktitle = {CVPR},
    year = {2015}
}