install env

conda create -n MCNet python=3.7

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

pip install opencv-python

pip install timm

pip install easydict

pip install transforms3d

pip install h5py

pip install open3d

run this for using our MCNet

CUDA_VISIBLE_DEVICES=0 python main-MCNet.py --test --output_points 10000 --num_multi_completion 100 --lambda 0.6 --ckpts ./experiments/trained_model/ckpt-MCNet.pth --config ./cfgs/ShapeNet34_models/MCNet.yaml --exp_name test_MCNet

Note that:

changing output_points for different point resolutions. output_points can be any number. e.g. 16, 32, 64, 128, 5000, 8000, 10000, 300000, etc.

changing num_multi_completion for a different number of completion results. num_multi_completion can be any number. e.g. 5, 10, 20, 50, 100, 200, etc.

changing lambda for different levels of completion diversity. lambda is from 0.0 to 1.0. e.g. 0.2, 0.54, 0.28,0.3, 0.4, 0.5, 0.62, etc.