ark1234 / lossdistillationweightedcd

The official repository of the paper "Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance"

Loss_Distillation_weighted_CD

The official repository of the IROS 2024 Oral paper "Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance"

UPDATE

SeedFormer + WeightedCDs

Installation

The code has been tested on one configuration:

• python == 3.6.8
• PyTorch == 1.8.1
• CUDA == 10.2
• numpy
• open3d

pip install -r requirements.txt

Compile the C++ extension modules:

sh install.sh

Datasets

The details of used datasets can be found in DATASET.md

Pretrained Models are attached

Usage

Training on PCN dataset

First, you should specify your dataset directories in train_pcn.py:

__C.DATASETS.SHAPENET.PARTIAL_POINTS_PATH        = '<*PATH-TO-YOUR-DATASET*>/PCN/%s/partial/%s/%s/%02d.pcd'
__C.DATASETS.SHAPENET.COMPLETE_POINTS_PATH       = '<*PATH-TO-YOUR-DATASET*>/PCN/%s/complete/%s/%s.pcd'

To train SeedFormer + HyperCD on PCN dataset, simply run:

python3 train_pcn.py

Testing on PCN dataset

To test a pretrained model, run:

python3 train_pcn.py --test

Or you can give the model directory name to test one particular model:

python3 train_pcn.py --test --pretrained train_pcn_Log_2022_XX_XX_XX_XX_XX

Save generated complete point clouds as well as gt and partial clouds in testing:

python3 train_pcn.py --test --output 1

Using ShapeNet-55/34

To use ShapeNet55 dataset, change the data directoriy in train_shapenet55.py:

__C.DATASETS.SHAPENET55.COMPLETE_POINTS_PATH     = '<*PATH-TO-YOUR-DATASET*>/ShapeNet55/shapenet_pc/%s'

Then, run:

python3 train_shapenet55.py

In order to switch to ShapeNet34, you can change the data file in train_shapenet55.py:

__C.DATASETS.SHAPENET55.CATEGORY_FILE_PATH       = './datasets/ShapeNet55-34/ShapeNet-34/'

The testing process is very similar to that on PCN:

python3 train_shapenet55.py --test

Acknowledgement

Code is borrowed from SeedFormer, Weighted losses can be found in loss_utils.py, All losses can be easily implement to other networks such as PointAttN and CP-Net.