Home

Awesome

Vote2Cap-DETR: A Set-to-Set Perspective Towards 3D Dense Captioning

Official implementation of "End-to-End 3D Dense Captioning with Vote2Cap-DETR" (CVPR 2023) and "Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning" (T-PAMI 2024).

pipeline

Thanks to the implementation of 3DETR, Scan2Cap, and VoteNet.

0. News

pipeline

1. Environment

Our code is tested with PyTorch 1.7.1, CUDA 11.0 and Python 3.8.13. Besides pytorch, this repo also requires the following Python dependencies:

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

If you wish to use multi-view feature extracted by Scan2Cap, you should also install h5py:

pip install h5py

It is also REQUIRED to compile the CUDA accelerated PointNet++, and compile gIoU support for fast training:

cd third_party/pointnet2
python setup.py install

cd utils
python cython_compile.py build_ext --inplace

To build support for METEOR metric for evaluating captioning performance, we also installed the java package.

2. Dataset Preparation

We follow Scan2Cap's procedure to prepare datasets under the ./data folder (Scan2CAD NOT required).

Preparing 3D point clouds from ScanNet. Download the ScanNetV2 dataset and change the SCANNET_DIR to the scans folder in data/scannet/batch_load_scannet_data.py, and run the following commands.

cd data/scannet/
python batch_load_scannet_data.py

Preparing Language Annotations. Please follow this to download the ScanRefer dataset, and put it under ./data.

[Optional] To prepare for Nr3D, it is also required to download and put the Nr3D under ./data. Since it's in .csv format, it is required to run the following command to process data.

cd data; python parse_nr3d.py

3. [Optional] Download Pretrained Weights

You can download all the ready-to-use weights from huggingface.

ModelSCSTrgbmulti-viewnormalcheckpoint
Vote2Cap-DETR-$\checkmark$-$\checkmark$[checkpoint]
Vote2Cap-DETR--$\checkmark$$\checkmark$[checkpoint]
Vote2Cap-DETR$\checkmark$$\checkmark$-$\checkmark$[checkpoint]
Vote2Cap-DETR$\checkmark$-$\checkmark$$\checkmark$[checkpoint]
Vote2Cap-DETR++-$\checkmark$-$\checkmark$[checkpoint]
Vote2Cap-DETR++--$\checkmark$$\checkmark$[checkpoint]
Vote2Cap-DETR++$\checkmark$$\checkmark$-$\checkmark$[checkpoint]
Vote2Cap-DETR++$\checkmark$-$\checkmark$$\checkmark$[checkpoint]

4. Training and Evaluation

Though we provide training commands from scratch, you can also start with some pretrained parameters provided under the ./pretrained folder and skip certain steps.

[optional] 4.0 Pre-Training for Detection

You are free to SKIP the following procedures as they are to generate the pre-trained weights in ./pretrained folder.

To train the Vote2Cap-DETR's detection branch for point cloud input without additional 2D features (aka [xyz + rgb + normal + height]):

bash scripts/vote2cap-detr/train_scannet.sh

# Please also try our updated Vote2Cap-DETR++ model:
bash scripts/vote2cap-detr++/train_scannet.sh

To evaluate the pre-trained detection branch on ScanNet:

bash scripts/vote2cap-detr/eval_scannet.sh

# Our updated Vote2Cap-DETR++:
bash scripts/vote2cap-detr++/eval_scannet.sh

To train with additional 2D features (aka [xyz + multiview + normal + height]) rather than RGB inputs, you can manually replace --use_color to --use_multiview.

4.1 MLE Training for 3D Dense Captioning

Please make sure there are pretrained checkpoints under the ./pretrained directory. To train the mdoel for 3D dense captioning with MLE training on ScanRefer:

bash scripts/vote2cap-detr/train_mle_scanrefer.sh

# Our updated Vote2Cap-DETR++:
bash scripts/vote2cap-detr++/train_mle_scanrefer.sh

And on Nr3D:

bash scripts/vote2cap-detr/train_mle_nr3d.sh

# Our updated Vote2Cap-DETR++:
bash scripts/vote2cap-detr++/train_mle_nr3d.sh

4.2 Self-Critical Sequence Training for 3D Dense Captioning

To train the model with Self-Critical Sequence Training (SCST), you can use the following command:

bash scripts/vote2cap-detr/train_scst_scanrefer.sh

# Our updated Vote2Cap-DETR++:
bash scripts/vote2cap-detr++/train_scst_scanrefer.sh

And on Nr3D:

bash scripts/vote2cap-detr/train_scst_nr3d.sh

# Our updated Vote2Cap-DETR++:
bash scripts/vote2cap-detr++/train_scst_nr3d.sh

4.3 Evaluating the Weights

You can evaluate any trained model with specified models and checkponts. Change --dataset scene_scanrefer to --dataset scene_nr3d to evaluate the model for the Nr3D dataset.

bash scripts/eval_3d_dense_caption.sh

Run the following commands to store object predictions and captions for each scene.

bash scripts/demo.sh

5. Make Predictions for online test benchmark

Our model also provides the inference code for ScanRefer online test benchmark.

The following command will generate a .json file under the folder defined by --checkpoint_dir.

bash submit.sh

6. BibTex

If you find our work helpful, please kindly cite our paper:

@inproceedings{chen2023end,
  title={End-to-end 3d dense captioning with vote2cap-detr},
  author={Chen, Sijin and Zhu, Hongyuan and Chen, Xin and Lei, Yinjie and Yu, Gang and Chen, Tao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11124--11133},
  year={2023}
}

@article{chen2024vote2cap,
  title={Vote2cap-detr++: Decoupling localization and describing for end-to-end 3d dense captioning},
  author={Chen, Sijin and Zhu, Hongyuan and Li, Mingsheng and Chen, Xin and Guo, Peng and Lei, Yinjie and Gang, YU and Li, Taihao and Chen, Tao},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2024},
  publisher={IEEE}
}

7. License

Vote2Cap-DETR and Vote2Cap-DETR++ are both licensed under a MIT License.

8. Contact

If you have any questions or suggestions regarding this repo, please feel free to open issues!