MaskPoint

[ECCV 2022] Masked Discrimination for Self-Supervised Learning on Point Clouds

Please check out our paper here.

Pretrained Models

Task	Dataset	Config	Acc.	Download
Pre-training	ShapeNet	pretrain_shapenet.yaml	--	{w/o,w/} MoCo
Classification	ScanObjectNN	finetune_scanobject_hardest.yaml	84.6%	here
Classification	ScanObjectNN	finetune_scanobject_objectbg.yaml	89.3%	here
Classification	ScanObjectNN	finetune_scanobject_objectonly.yaml	89.7%	here
Classification	ModelNet40	finetune_modelnet.yaml	93.8%	here

3D Object Detection

Task	Dataset	Config	AP25	AP50	Download
Pre-training	ScanNet-Medium	pretrain_scannet_enc3x.yaml	--	--	here
Pre-training	ScanNet-Medium	pretrain_scannet_enc12x.yaml	--	--	here
Detection	ScanNetV2	finetune_scannetv2_enc3x.sh	63.4	40.6	here
Detection	ScanNetV2	finetune_scannetv2_enc12x.sh	64.2	42.1	here

Usage

Requirements

PyTorch >= 1.7.0
python >= 3.7
CUDA >= 9.0
GCC >= 4.9
torchvision

pip install -r requirements.txt
bash install.sh

Dataset

For ModelNet40, ScanObjectNN, and ShapeNetPart datasets, we use ShapeNet for the pre-training of MaskPoint models, and then finetune on these datasets respectively.

For ScanNetV2 object detection dataset, we use ScanNet-Medium for the pre-training. Please refer to the paper Sec. 4 [Pretraining Datasets] for details.

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

MaskPoint pre-training

To pre-train the MaskPoint models on ShapeNet, simply run:

python main.py --config cfgs/pretrain_shapenet.yaml \
    --exp_name pretrain_shapenet \
    [--val_freq 10]

val_freq controls the frequence to evaluate the Transformer on ModelNet40 with LinearSVM.

Similarly, to pre-train the MaskPoint models on ScanNet-Medium, simply run:

# Pretrain 3x encoder model
python main.py --config cfgs/pretrain_scannet_enc3x.yaml \
    --exp_name pretrain_scannet_enc3x \
    [--val_freq 10]

# Pretrain 12x encoder model
python main.py --config cfgs/pretrain_scannet_enc12x.yaml \
    --exp_name pretrain_scannet_enc12x \
    [--val_freq 10]

Fine-tuning on downstream tasks

We finetune our MaskPoint on 5 downstream tasks: Classfication on ModelNet40, Few-shot learning on ModelNet40, Transfer learning on ScanObjectNN, Part segmentation on ShapeNetPart, and Object detection on ScanNetV2.

ModelNet40

To finetune a pre-trained MaskPoint model on ModelNet40, simply run:

python main.py
    --config cfgs/finetune_modelnet.yaml \
    --finetune_model \
    --ckpts <path> \
    --exp_name <name>

To evaluate a model finetuned on ModelNet40, simply run:

bash ./scripts/test.sh <GPU_IDS>\
    --config cfgs/finetune_modelnet.yaml \
    --ckpts <path> \
    --exp_name <name>

Few-shot Learning on ModelNet40

We follow the few-shot setting in the previous work.

First, generate your own few-shot learning split or use the same split as us (see DATASET.md).

# generate few-shot learning split
cd datasets/
python generate_few_shot_data.py
# train and evaluate the MaskPoint
python main.py \
    --config cfgs/fewshot_modelnet.yaml \
    --finetune_model \
    --ckpts <path> \
    --exp_name <name> \
    --way <int> \
    --shot <int> \
    --fold <int>

ScanObjectNN

To finetune a pre-trained MaskPoint model on ScanObjectNN, simply run:

python main.py \
    --config cfgs/finetune_scanobject_hardest.yaml \
    --finetune_model \
    --ckpts <path> \
    --exp_name <name>

To evaluate a model on ScanObjectNN, simply run:

bash ./scripts/test_scan.sh <GPU_IDS>\
    --config cfgs/finetune_scanobject_hardest.yaml \
    --ckpts <path> \
    --exp_name <name>

ScanNetV2

See MaskPoint -- 3DETR Finetuning for detailed instructions.

ShapeNetPart

Coming soon..

Citation

@article{liu2022masked,
  title={Masked Discrimination for Self-Supervised Learning on Point Clouds},
  author={Liu, Haotian and Cai, Mu and Lee, Yong Jae},
  journal={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2022}
}

haotian-liu / MaskPoint