Tianxinhuang / CALoss

The TF codes for Learning to Measure the Point Cloud Reconstruction Loss in a Representation Space[CVPR'23]

CALoss

[CVPR'23] The codes for Learning to Measure the Point Cloud Reconstruction Loss in a Representation Space

Environment

• TensorFlow 1.13.1
• Cuda 10.0
• Python 3.6.9
• numpy 1.14.5

We also provide an available conda environment (lcd.yaml) in this repo. Please run:

conda env create -f caloss.yaml

Dataset

The adopted ShapeNet Part dataset is adopted following FoldingNet, while the ModelNet10 and ModelNet40 datasets follow PointNet. Other datasets can also be used. Just revise the path by the (--filepath) parameter when training or evaluating the networks. The files in (--filepath) should be organized as

    <filepath>
    ├── <trainfile1>.h5 
    ├── <trainfile2>.h5
    ├── ...
    ├── train_files.txt
    └── test_files.txt

where the contents in (train_files.txt) or (test_files.txt) should include the directory of training or testing h5 files, such as:

    train_files.txt
    ├── <trainfile1>.h5
    ├── <trainfile2>.h5
    ├── ...

We also provide the processed datasets in Google Drive. You can just download, unzip them, and just set filepath to one of the dataset paths.

Usage

1. Preparation

cd ./tf_ops
bash compile.sh

2. Train

For the reconstruction task,

Python3 vv_cae.py

Note that the paths of data should be edited through the (--filepath) parameter according to your setting. For example, if we use the download dataset (./objdata/ShapeNet_part), the training command would be

Python3 vv_cae.py --filepath ./objdata/ShapeNet_part

3. Test

For the evaluation of reconstruction errors,

Python3 vvae_eva.py 

The trained weight files should be provided by the (--savepath) parameter to evaluate the performances.

Here, we also provide weights of the reconstruction network AE pre-trained on ShapeNet Part dataset. To evaluate its performance, just download and unzip it, then change the savepath to its folder. If the dataset and weights are put in (./objdata/ShapeNet_part) and (./pnae), respectively, the command would be

Python3 vvae_eva.py --filepath ./objdata/ShapeNet_part --savepath ./pnae