MaskPoint -- 3DETR Finetuning

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

Haotian Liu, Mu Cai, Yong Jae Lee

Please check out our paper here.

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This repo is inherited from the official repo 3DETR. We add the functionality of loading pretrained weights from MaskPoint. The pretraining code can be found here. Major modifications are at main.py. You should prepare the data and environment as the original repo.

To start, put the downloaded checkpoints into the folder checkpoints.

To train and validate the standard 3DETR model (3 encoder layers) and the larger 3DETR model (12 encoder layers), use the following weights and scripts:

# Encoder layer	Training script	Pretrained weights	Test script	Finetuned weights	mAP(0.25)	mAP(0.50)
3	train_enc3x.sh	here	test_enc3x.sh	here	63.4	40.6
12	train_enc12x.sh	here	test_enc12x.sh	here	64.2	42.1

Citation

@article{liu2022masked,
  title={Masked Discrimination for Self-Supervised Learning on Point Clouds},
  author={Liu, Haotian and Cai, Mu and Lee, Yong Jae},
  journal={arXiv preprint arXiv:2203.11183},
  year={2022}
}

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3DETR: An End-to-End Transformer Model for 3D Object Detection

PyTorch implementation and models for 3DETR.

3DETR (3D DEtection TRansformer) is a simpler alternative to complex hand-crafted 3D detection pipelines. It does not rely on 3D backbones such as PointNet++ and uses few 3D-specific operators. 3DETR obtains comparable or better performance than 3D detection methods such as VoteNet. The encoder can also be used for other 3D tasks such as shape classification. More details in the paper "An End-to-End Transformer Model for 3D Object Detection".

[website] [arXiv] [bibtex]

Code description. Our code is based on prior work such as DETR and VoteNet and we aim for simplicity in our implementation. We hope it can ease research in 3D detection.

Pretrained Models

We provide the pretrained model weights and the corresponding metrics on the val set (per class APs, Recalls). We provide a Python script utils/download_weights.py to easily download the weights/metrics files.

Arch	Dataset	Epochs	AP25	AP50	Model weights	Eval metrics
3DETR-m	SUN RGB-D	1080	59.1	30.3	weights	metrics
3DETR	SUN RGB-D	1080	58.0	30.3	weights	metrics
3DETR-m	ScanNet	1080	65.0	47.0	weights	metrics
3DETR	ScanNet	1080	62.1	37.9	weights	metrics

Model Zoo

For convenience, we provide model weights for 3DETR trained for different number of epochs.

Arch	Dataset	Epochs	AP25	AP50	Model weights	Eval metrics
3DETR-m	SUN RGB-D	90	51.0	22.0	weights	metrics
3DETR-m	SUN RGB-D	180	55.6	27.5	weights	metrics
3DETR-m	SUN RGB-D	360	58.2	30.6	weights	metrics
3DETR-m	SUN RGB-D	720	58.1	30.4	weights	metrics
3DETR	SUN RGB-D	90	43.7	16.2	weights	metrics
3DETR	SUN RGB-D	180	52.1	25.8	weights	metrics
3DETR	SUN RGB-D	360	56.3	29.6	weights	metrics
3DETR	SUN RGB-D	720	56.0	27.8	weights	metrics
3DETR-m	ScanNet	90	47.1	19.5	weights	metrics
3DETR-m	ScanNet	180	58.7	33.6	weights	metrics
3DETR-m	ScanNet	360	62.4	37.7	weights	metrics
3DETR-m	ScanNet	720	63.7	44.5	weights	metrics
3DETR	ScanNet	90	42.8	15.3	weights	metrics
3DETR	ScanNet	180	54.5	28.8	weights	metrics
3DETR	ScanNet	360	59.0	35.4	weights	metrics
3DETR	ScanNet	720	61.1	40.2	weights	metrics

Running 3DETR

Installation

Our code is tested with PyTorch 1.9.0, CUDA 10.2 and Python 3.6. It may work with other versions.

You will need to install pointnet2 layers by running

cd third_party/pointnet2 && python setup.py install

You will also need Python dependencies (either conda install or pip install)

matplotlib
opencv-python
plyfile
'trimesh>=2.35.39,<2.35.40'
'networkx>=2.2,<2.3'
scipy

Some users have experienced issues using CUDA 11 or higher. Please try using CUDA 10.2 if you run into CUDA issues.

Optionally, you can install a Cythonized implementation of gIOU for faster training.

conda install cython
cd utils && python cython_compile.py build_ext --inplace

Benchmarking

Dataset preparation

We follow the VoteNet codebase for preprocessing our data. The instructions for preprocessing SUN RGB-D are here and ScanNet are here.

You can edit the dataset paths in datasets/sunrgbd.py and datasets/scannet.py or choose to specify at runtime.

Testing

Once you have the datasets prepared, you can test pretrained models as

python main.py --dataset_name <dataset_name> --nqueries <number of queries> --test_ckpt <path_to_checkpoint> --test_only [--enc_type masked]

We use 128 queries for the SUN RGB-D dataset and 256 queries for the ScanNet dataset. You will need to add the flag --enc_type masked when testing the 3DETR-m checkpoints. Please note that the testing process is stochastic (due to randomness in point cloud sampling and sampling the queries) and so results can vary within 1% AP25 across runs. This stochastic nature of the inference process is also common for methods such as VoteNet.

If you have not edited the dataset paths for the files in the datasets folder, you can pass the path to the datasets using the --dataset_root_dir flag.

Training

The model can be simply trained by running main.py.

python main.py --dataset_name <dataset_name> --checkpoint_dir <path to store outputs>

To reproduce the results in the paper, we provide the arguments in the scripts folder. A variance of 1% AP25 across different training runs can be expected.

You can quickly verify your installation by training a 3DETR model for 90 epochs on ScanNet following the file scripts/scannet_quick.sh and compare it to the pretrained checkpoint from the Model Zoo.

License

The majority of 3DETR is licensed under the Apache 2.0 license as found in the LICENSE file, however portions of the project are available under separate license terms: licensing information for pointnet2 is available at https://github.com/erikwijmans/Pointnet2_PyTorch/blob/master/UNLICENSE

Contributing

We welcome your pull requests! Please see CONTRIBUTING and CODE_OF_CONDUCT for more info.

Citation

If you find this repository useful, please consider starring ⭐ us and citing

@inproceedings{misra2021-3detr,
    title={{An End-to-End Transformer Model for 3D Object Detection}},
    author={Misra, Ishan and Girdhar, Rohit and Joulin, Armand},
    booktitle={{ICCV}},
    year={2021},
}