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charlesq34 / pointnet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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How to use my own data for part segmentation? #94

Closed IsaacGuan closed 6 years ago

IsaacGuan commented 6 years ago

Thank you so much for sharing the code! In a previous issue How to prepare my own data set for the segmentation training? you mentioned that we can reference the script here: https://github.com/charlesq34/3dmodel_feature/blob/master/io/write_hdf5.py to generate the HDF5 files. I looked into that code, it only takes two types of information to the HDF5 file, which are h5_batch_data and h5_batch_label. However, for part segmentation we should also introduce an additional dimension which includes the part information. And actually the structure of HDF5 file for part segmentation looks like this: screenshot from 2018-03-28 22-07-01 The view of "pid" contains the part information for each point cloud. But the problem is how can this be written to HDF5 files? As I have some ready made segmented point clouds with extra channel representing the parts in the structure of (X, Y, Z, Pid), I really want to use your implementation to explore further on my own dataset. The "write_hdf5.py" has very few comments in it, so I would be really appreciated if you could give me some hints on this issue!

IsaacGuan commented 6 years ago

I think I have found one possible approach. With a simple tool called plyfile, we can read the points from .ply file as a numpy array. Then we simply write these arrays into HDF5 files using h5py library. This is how I did: https://github.com/IsaacGuan/PointNet-Plane-Detection/blob/master/data/write_hdf5.py

sivaprasadraju commented 5 years ago

Hello All, I wanted to train PointNet for classification only. I have pointcloud data(x, y, z, i) in .bin format and corresponding labels in .txt format.

Can anyone please help me how to train PointNet with this data?

Dawn-LLL commented 4 years ago

Have you solved your question? If you like, can I ask you questions My qq1922797937 thank you very much