This repository contains PyTorch implementation for SD-Net: Spatially-Disentangled Point Cloud Completion Network (ACM MM 2023 Oral Presentation).
pip install -r requirements.txt
NOTE: PyTorch >= 1.7 and GCC >= 4.9 are required.
# Chamfer Distance
bash install.sh
The solution for a common bug in chamfer distance installation can be found in Issue #6
# PointNet++
pip install "git+git://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"
# GPU kNN
pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl
The details of our new ShapeNet-55/34 datasets and other existing datasets can be found in DATASET.md.
To evaluate a pre-trained SDNet model on the Dataset with single GPU, run:
bash ./scripts/test.sh <GPU_IDS> \
--ckpts <path> \
--config <config> \
--exp_name <name> \
[--mode <easy/median/hard>]
Test the SDNet pretrained model on ShapeNet55 benchmark (easy mode):
bash ./scripts/test.sh 0 \
--ckpts ./pretrained/SDNet_ShapeNet55.pth \
--config ./cfgs/ShapeNet55_models/PoinTr.yaml \
--mode easy \
--exp_name example
To train a point cloud completion model from scratch, run:
# Use DistributedDataParallel (DDP)
bash ./scripts/dist_train.sh <NUM_GPU> <port> \
--config <config> \
--exp_name <name> \
[--resume] \
[--start_ckpts <path>] \
[--val_freq <int>]
# or just use DataParallel (DP)
bash ./scripts/train.sh <GPUIDS> \
--config <config> \
--exp_name <name> \
[--resume] \
[--start_ckpts <path>] \
[--val_freq <int>]
Train a SDNet model on ShapeNet55 benchmark with 2 gpus:
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/dist_train.sh 2 13232 \
--config ./cfgs/ShapeNet55_models/SDNet.yaml \
--exp_name example
Resume a checkpoint:
CUDA_VISIBLE_DEVICES=0,1 bash ./scripts/dist_train.sh 2 13232 \
--config ./cfgs/ShapeNet55_models/SDNet.yaml \
--exp_name example --resume
Train a SDNet model with a single GPU:
bash ./scripts/train.sh 0 \
--config ./cfgs/ShapeNet55_models/SDNet.yaml \
--exp_name example
Our code is inspired by PoinTr, SnowflakeNet and SeedFormer.
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