This repository contains code for the paper Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions, published at ECCV 2020.

Thanks to yanx27 for an excellent PyTorch PointNet++ implementation Pointnet_Pointnet2_pytorch; our model implementation is based off that codebase.

If you find this codebase useful, please consider citing:

@inProceedings{selfsupacd,
  title={Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions},
  author = {Matheus Gadelha and Aruni RoyChowdhury and Gopal Sharma and Evangelos Kalogerakis and Liangliang Cao and Erik Learned-Miller and Rui Wang and Subhransu Maji},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2020}
}

CUDA setup:

CUDA: '9.2.148'    # torch.version.cuda
CuDNN: 7603        # torch.backends.cudnn.version()

Conda environment:

conda create -n acd-env python=3.6
pip install numpy six protobuf>=3.2.0
pip install torch torchvision
pip install matplotlib tqdm tensorboard_logger trimesh

For reference, we also tested using CUDA 10.1, and the corresponding torch and torchvision can be installed using pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html.

Download part segmentation dataset ShapeNet here and save in data/shapenetcore_partanno_segmentation_benchmark_v0_normal/.

Download the pre-computed ACD components for the unlabeled ShapeNet core shapes from here and extract its content in data.

Download the aligned and resampled ModelNet40 dataset for shape classication from here and save in data/modelnet40_normal_resampled/.

From the project root, the following code snippet trained a model jointly on semantic segmentation on ShapeNetSeg, using 5 samples per shape category (i.e. 5 * 16 = 80 labeled training samples) and a pairwise contrastive loss over ACD components of the unlabeled ShapeNet Core data (for 9 epochs, decaying the learning rate at every epoch, with a batchsize of 24 shapes).

python train_partseg_shapenet_multigpu.py --seed 2001 \
        --k_shot 5 --batch_size 24 --selfsup --step_size 1  --epoch 9 \
        --ss_path data/ACDv2/

The models are stored in the experiment output folder, under checkpoints sub-folder. Tensorboard logs and console output as txt file are saved under sub-folder logs. The test performance is evaluated at the end of the training epochs (i.e. epoch 9 in this case) and written to the logfile.

Pretraining on ACD:

The following example command trains a PointNet++ network on the ACD task. The seed is an integer that serves as an identifier for multiple runs of the same experiment. Random rotations around the "up" or Z axis is done as data augmentation during training(--rotation_z). Only the best performing model based on the validation ACD loss is stored under the experiment output folder, under checkpoints sub-folder. Tensorboard logs and console output as txt file are saved under sub-folder logs.

python pretrain_partseg_shapenet.py --rotation_z --seed 1001 --gpu 0 \
                                    --model pointnet2_part_seg_msg  \
                                    --batch_size 16 --step_size 1  \
                                    --selfsup  --retain_overlaps \
                                    --ss_path data/ACDv2

Evaluate pre-trained model on ModelNet40:

• Evaluating on ModelNet with cross-validation of SVM (takes a while): python test_acdfeat_modelnet.py --gpu 0 --sqrt --model pointnet2_part_seg_msg --log_dir $LOG_DIR --cross_val_svm
• Avoiding the cross-validation for the SVM C, one can also explicitly put the value as a runtime argument: python test_acdfeat_modelnet.py --gpu 0 --sqrt --model pointnet2_part_seg_msg --log_dir $LOG_DIR --svm_c 220.0
• Examples of LOG_DIR can be found at the top of the test_acdfeat_modelnet.py code file. Basically it points to wherever the ACD pre-training script dumps its outputs.