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".
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.
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 |
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 |
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
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.
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.
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.
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
We welcome your pull requests! Please see CONTRIBUTING and CODE_OF_CONDUCT for more info.
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},
}